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Sample records for kaempferol multivariate analysis

  1. Multivariate analysis with LISREL

    CERN Document Server

    Jöreskog, Karl G; Y Wallentin, Fan

    2016-01-01

    This book traces the theory and methodology of multivariate statistical analysis and shows how it can be conducted in practice using the LISREL computer program. It presents not only the typical uses of LISREL, such as confirmatory factor analysis and structural equation models, but also several other multivariate analysis topics, including regression (univariate, multivariate, censored, logistic, and probit), generalized linear models, multilevel analysis, and principal component analysis. It provides numerous examples from several disciplines and discusses and interprets the results, illustrated with sections of output from the LISREL program, in the context of the example. The book is intended for masters and PhD students and researchers in the social, behavioral, economic and many other sciences who require a basic understanding of multivariate statistical theory and methods for their analysis of multivariate data. It can also be used as a textbook on various topics of multivariate statistical analysis.

  2. Methods of Multivariate Analysis

    CERN Document Server

    Rencher, Alvin C

    2012-01-01

    Praise for the Second Edition "This book is a systematic, well-written, well-organized text on multivariate analysis packed with intuition and insight . . . There is much practical wisdom in this book that is hard to find elsewhere."-IIE Transactions Filled with new and timely content, Methods of Multivariate Analysis, Third Edition provides examples and exercises based on more than sixty real data sets from a wide variety of scientific fields. It takes a "methods" approach to the subject, placing an emphasis on how students and practitioners can employ multivariate analysis in real-life sit

  3. Applied multivariate statistical analysis

    CERN Document Server

    Härdle, Wolfgang Karl

    2015-01-01

    Focusing on high-dimensional applications, this 4th edition presents the tools and concepts used in multivariate data analysis in a style that is also accessible for non-mathematicians and practitioners.  It surveys the basic principles and emphasizes both exploratory and inferential statistics; a new chapter on Variable Selection (Lasso, SCAD and Elastic Net) has also been added.  All chapters include practical exercises that highlight applications in different multivariate data analysis fields: in quantitative financial studies, where the joint dynamics of assets are observed; in medicine, where recorded observations of subjects in different locations form the basis for reliable diagnoses and medication; and in quantitative marketing, where consumers’ preferences are collected in order to construct models of consumer behavior.  All of these examples involve high to ultra-high dimensions and represent a number of major fields in big data analysis. The fourth edition of this book on Applied Multivariate ...

  4. Multivariate Quantitative Chemical Analysis

    Science.gov (United States)

    Kinchen, David G.; Capezza, Mary

    1995-01-01

    Technique of multivariate quantitative chemical analysis devised for use in determining relative proportions of two components mixed and sprayed together onto object to form thermally insulating foam. Potentially adaptable to other materials, especially in process-monitoring applications in which necessary to know and control critical properties of products via quantitative chemical analyses of products. In addition to chemical composition, also used to determine such physical properties as densities and strengths.

  5. Multivariate Quantitative Chemical Analysis

    Science.gov (United States)

    Kinchen, David G.; Capezza, Mary

    1995-01-01

    Technique of multivariate quantitative chemical analysis devised for use in determining relative proportions of two components mixed and sprayed together onto object to form thermally insulating foam. Potentially adaptable to other materials, especially in process-monitoring applications in which necessary to know and control critical properties of products via quantitative chemical analyses of products. In addition to chemical composition, also used to determine such physical properties as densities and strengths.

  6. Multivariate data analysis

    DEFF Research Database (Denmark)

    Hansen, Michael Adsetts Edberg

    Interest in statistical methodology is increasing so rapidly in the astronomical community that accessible introductory material in this area is long overdue. This book fills the gap by providing a presentation of the most useful techniques in multivariate statistics. A wide-ranging annotated set...

  7. Multivariate data analysis

    DEFF Research Database (Denmark)

    Hansen, Michael Adsetts Edberg

    Interest in statistical methodology is increasing so rapidly in the astronomical community that accessible introductory material in this area is long overdue. This book fills the gap by providing a presentation of the most useful techniques in multivariate statistics. A wide-ranging annotated set...

  8. Multivariate analysis in thoracic research.

    Science.gov (United States)

    Mengual-Macenlle, Noemí; Marcos, Pedro J; Golpe, Rafael; González-Rivas, Diego

    2015-03-01

    Multivariate analysis is based in observation and analysis of more than one statistical outcome variable at a time. In design and analysis, the technique is used to perform trade studies across multiple dimensions while taking into account the effects of all variables on the responses of interest. The development of multivariate methods emerged to analyze large databases and increasingly complex data. Since the best way to represent the knowledge of reality is the modeling, we should use multivariate statistical methods. Multivariate methods are designed to simultaneously analyze data sets, i.e., the analysis of different variables for each person or object studied. Keep in mind at all times that all variables must be treated accurately reflect the reality of the problem addressed. There are different types of multivariate analysis and each one should be employed according to the type of variables to analyze: dependent, interdependence and structural methods. In conclusion, multivariate methods are ideal for the analysis of large data sets and to find the cause and effect relationships between variables; there is a wide range of analysis types that we can use.

  9. Multivariate data analysis

    Digital Repository Service at National Institute of Oceanography (India)

    Fernandes, A.A.; Antony, M.K.; Somayajulu, Y.K.; Sarma, Y.V.B.; Almeida, A.M.; Mahadevan, R.

    predictability. Predictors, which are poorly connected with the predictand, may be excluded subjectively by a process of screening. For exam- ple, in ocean-atmospheric forecasting problems the predictors, which are not dynamically related to the predictands... aij, and is square and anti symmetric (i.e., aji =aasteriskmathij, where ?*? indicates the complex conjugate). While Complex EOF analysis can be used to study the characteristics of propagating waves as well as standing waves, EOF analysis is useful...

  10. Practical multivariate analysis

    CERN Document Server

    Afifi, Abdelmonem; Clark, Virginia A

    2011-01-01

    ""First of all, it is very easy to read. … The authors manage to introduce and (at least partially) explain even quite complex concepts, e.g. eigenvalues, in an easy and pedagogical way that I suppose is attractive to readers without deeper statistical knowledge. The text is also sprinkled with references for those who want to probe deeper into a certain topic. Secondly, I personally find the book's emphasis on practical data handling very appealing. … Thirdly, the book gives very nice coverage of regression analysis. … this is a nicely written book that gives a good overview of a large number

  11. Exploratory and multivariate data analysis

    CERN Document Server

    Jambu, Michel

    1991-01-01

    With a useful index of notations at the beginning, this book explains and illustrates the theory and application of data analysis methods from univariate to multidimensional and how to learn and use them efficiently. This book is well illustrated and is a useful and well-documented review of the most important data analysis techniques.Key Features* Describes, in detail, exploratory data analysis techniques from the univariate to the multivariate ones* Features a complete description of correspondence analysis and factor analysis techniques as multidimensional statistical data a

  12. Multivariate image analysis in biomedicine.

    Science.gov (United States)

    Nattkemper, Tim W

    2004-10-01

    In recent years, multivariate imaging techniques are developed and applied in biomedical research in an increasing degree. In research projects and in clinical studies as well m-dimensional multivariate images (MVI) are recorded and stored to databases for a subsequent analysis. The complexity of the m-dimensional data and the growing number of high throughput applications call for new strategies for the application of image processing and data mining to support the direct interactive analysis by human experts. This article provides an overview of proposed approaches for MVI analysis in biomedicine. After summarizing the biomedical MVI techniques the two level framework for MVI analysis is illustrated. Following this framework, the state-of-the-art solutions from the fields of image processing and data mining are reviewed and discussed. Motivations for MVI data mining in biology and medicine are characterized, followed by an overview of graphical and auditory approaches for interactive data exploration. The paper concludes with summarizing open problems in MVI analysis and remarks upon the future development of biomedical MVI analysis.

  13. Essentials of multivariate data analysis

    CERN Document Server

    Spencer, Neil H

    2013-01-01

    ""… this text provides an overview at an introductory level of several methods in multivariate data analysis. It contains in-depth examples from one data set woven throughout the text, and a free [Excel] Add-In to perform the analyses in Excel, with step-by-step instructions provided for each technique. … could be used as a text (possibly supplemental) for courses in other fields where researchers wish to apply these methods without delving too deeply into the underlying statistics.""-The American Statistician, February 2015

  14. Multivariable modeling and multivariate analysis for the behavioral sciences

    CERN Document Server

    Everitt, Brian S

    2009-01-01

    Multivariable Modeling and Multivariate Analysis for the Behavioral Sciences shows students how to apply statistical methods to behavioral science data in a sensible manner. Assuming some familiarity with introductory statistics, the book analyzes a host of real-world data to provide useful answers to real-life issues.The author begins by exploring the types and design of behavioral studies. He also explains how models are used in the analysis of data. After describing graphical methods, such as scatterplot matrices, the text covers simple linear regression, locally weighted regression, multip

  15. Multivariate Generalized Multiscale Entropy Analysis

    Directory of Open Access Journals (Sweden)

    Anne Humeau-Heurtier

    2016-11-01

    Full Text Available Multiscale entropy (MSE was introduced in the 2000s to quantify systems’ complexity. MSE relies on (i a coarse-graining procedure to derive a set of time series representing the system dynamics on different time scales; (ii the computation of the sample entropy for each coarse-grained time series. A refined composite MSE (rcMSE—based on the same steps as MSE—also exists. Compared to MSE, rcMSE increases the accuracy of entropy estimation and reduces the probability of inducing undefined entropy for short time series. The multivariate versions of MSE (MMSE and rcMSE (MrcMSE have also been introduced. In the coarse-graining step used in MSE, rcMSE, MMSE, and MrcMSE, the mean value is used to derive representations of the original data at different resolutions. A generalization of MSE was recently published, using the computation of different moments in the coarse-graining procedure. However, so far, this generalization only exists for univariate signals. We therefore herein propose an extension of this generalized MSE to multivariate data. The multivariate generalized algorithms of MMSE and MrcMSE presented herein (MGMSE and MGrcMSE, respectively are first analyzed through the processing of synthetic signals. We reveal that MGrcMSE shows better performance than MGMSE for short multivariate data. We then study the performance of MGrcMSE on two sets of short multivariate electroencephalograms (EEG available in the public domain. We report that MGrcMSE may show better performance than MrcMSE in distinguishing different types of multivariate EEG data. MGrcMSE could therefore supplement MMSE or MrcMSE in the processing of multivariate datasets.

  16. Multivariate Longitudinal Analysis with Bivariate Correlation Test.

    Science.gov (United States)

    Adjakossa, Eric Houngla; Sadissou, Ibrahim; Hounkonnou, Mahouton Norbert; Nuel, Gregory

    2016-01-01

    In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more general expressions of the model's parameters estimators. These estimators can be used in the framework of the multivariate longitudinal data analysis as well as in the more general context of the analysis of multivariate multilevel data. By using a likelihood ratio test, we test the significance of the correlations between the random effects of two dependent variables of the model, in order to investigate whether or not it is useful to model these dependent variables jointly. Simulation studies are done to assess both the parameter recovery performance of the EM estimators and the power of the test. Using two empirical data sets which are of longitudinal multivariate type and multivariate multilevel type, respectively, the usefulness of the test is illustrated.

  17. Multivariate meta-analysis: potential and promise.

    Science.gov (United States)

    Jackson, Dan; Riley, Richard; White, Ian R

    2011-09-10

    The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta-analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and discuss their advantages and disadvantages, we organized a one day 'Multivariate meta-analysis' event at the Royal Statistical Society. In addition to disseminating the most recent developments, we also received an abundance of comments, concerns, insights, critiques and encouragement. This article provides a balanced account of the day's discourse. By giving others the opportunity to respond to our assessment, we hope to ensure that the various view points and opinions are aired before multivariate meta-analysis simply becomes another widely used de facto method without any proper consideration of it by the medical statistics community. We describe the areas of application that multivariate meta-analysis has found, the methods available, the difficulties typically encountered and the arguments for and against the multivariate methods, using four representative but contrasting examples. We conclude that the multivariate methods can be useful, and in particular can provide estimates with better statistical properties, but also that these benefits come at the price of making more assumptions which do not result in better inference in every case. Although there is evidence that multivariate meta-analysis has considerable potential, it must be even more carefully applied than its univariate counterpart in practice. Copyright © 2011 John Wiley & Sons, Ltd.

  18. Exploratory multivariate analysis by example using R

    CERN Document Server

    Husson, Francois; Pages, Jerome

    2010-01-01

    Full of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R focuses on four fundamental methods of multivariate exploratory data analysis that are most suitable for applications. It covers principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, and hierarchical cluster analysis.The authors take a geometric point of view that provides a unified vision for exploring multivariate data tables. Within this framework, they present the prin

  19. Detrended fluctuation analysis of multivariate time series

    Science.gov (United States)

    Xiong, Hui; Shang, P.

    2017-01-01

    In this work, we generalize the detrended fluctuation analysis (DFA) to the multivariate case, named multivariate DFA (MVDFA). The validity of the proposed MVDFA is illustrated by numerical simulations on synthetic multivariate processes, where the cases that initial data are generated independently from the same system and from different systems as well as the correlated variate from one system are considered. Moreover, the proposed MVDFA works well when applied to the multi-scale analysis of the returns of stock indices in Chinese and US stock markets. Generally, connections between the multivariate system and the individual variate are uncovered, showing the solid performances of MVDFA and the multi-scale MVDFA.

  20. Kaempferol inhibits thrombosis and platelet activation.

    Science.gov (United States)

    Choi, Jun-Hui; Park, Se-Eun; Kim, Sung-Jun; Kim, Seung

    2015-08-01

    The objectives of the present study were to investigate whether kaempferol affects pro-coagulant proteinase activity, fibrin clot formation, blood clot and thrombin (or collagen/epinephrine)-stimulated platelet activation, thrombosis, and coagulation in ICR (Imprinting Control Region) mice and SD (Sprague-Dawley) rats. Kaempferol significantly inhibited the enzymatic activities of thrombin and FXa by 68 ± 1.6% and 52 ± 2.4%, respectively. Kaempferol also inhibited fibrin polymer formation in turbidity. Microscopic analysis was performed using a fluorescent conjugate. Kaempferol completely attenuated phosphorylation of extracellular signal-regulated kinase (ERK) 1/2, p38, c-Jun N-terminal kinase (JNK) 1/2, and phosphoinositide 3-kinase (PI3K)/PKB (AKT) in thrombin-stimulated platelets and delayed aggregation time (clotting) by 34.6% in an assay of collagen/epinephrine-stimulated platelet activation. Moreover, kaempferol protected against thrombosis development in 3 animal models, including collagen/epinephrine- and thrombin-induced acute thromboembolism models and an FeCl3-induced carotid arterial thrombus model. The ex vivo anticoagulant effect of kaempferol was further confirmed in ICR mice. This study demonstrated that kaempferol may be clinically useful due to its ability to reduce or prevent thrombotic challenge.

  1. Multivariate analysis of bistable flow; Analisis multivariable de flujo biestable

    Energy Technology Data Exchange (ETDEWEB)

    Castillo D, R.; Ortiz V, J.; Ruiz E, J.A. [ININ, 52750 La Marquesa, Estado de Mexico (Mexico); Calleros M, G. [CFE, Alto LUcero, Veracruz (Mexico)]. e-mail: rcd@nuclear.inin.mx

    2007-07-01

    In this work a bistable flow analysis with an autoregressive multivariate analysis is presented. The bistable flow happens in the boiling water nuclear reactors with external recirculation pumps, and it is presented in the bolster of discharge of the recirculation knot toward the central jet pumps. The phenomenon has two flow patterns, one with greater hydraulic lost that the other one. To irregular time intervals, the flow changes pattern in a random way. The program NOISE that it is in development in the ININ was used and that it uses a autoregressive multivariate model to determine the autoregression coefficients that contain the dynamic information of the signals and that later on they are used to obtain the relative contribution of power, which allows to settle down the influence that exists among the different analyzed variables. It was analyzed an event of bistable flow happened in a BWR5 to operation conditions of 80% power and 69% of total flow through the core. The signal flow noise in each one of the 20 jet pumps, of the power of a monitor of power average, of the motive flows of recirculation, of the controllers and of the position of the control valves in the knots, of the signals of the instrumentation of the recirculation pumps (power, current, pressure drop and suction temperature), and of the buses of where they take the feeding voltage the motors of the pumps. Among the main results it was found that the phenomenon of bistable flow affects to the pressure drop in the recirculation pump of the knot in that occur, what affects to the motor flow in the knot by what the opening system of the flow control valve of recirculation of the knot responds. (Author)

  2. Basics of Multivariate Analysis in Neuroimaging Data

    Science.gov (United States)

    Habeck, Christian Georg

    2010-01-01

    Multivariate analysis techniques for neuroimaging data have recently received increasing attention as they have many attractive features that cannot be easily realized by the more commonly used univariate, voxel-wise, techniques1,5,6,7,8,9. Multivariate approaches evaluate correlation/covariance of activation across brain regions, rather than proceeding on a voxel-by-voxel basis. Thus, their results can be more easily interpreted as a signature of neural networks. Univariate approaches, on the other hand, cannot directly address interregional correlation in the brain. Multivariate approaches can also result in greater statistical power when compared with univariate techniques, which are forced to employ very stringent corrections for voxel-wise multiple comparisons. Further, multivariate techniques also lend themselves much better to prospective application of results from the analysis of one dataset to entirely new datasets. Multivariate techniques are thus well placed to provide information about mean differences and correlations with behavior, similarly to univariate approaches, with potentially greater statistical power and better reproducibility checks. In contrast to these advantages is the high barrier of entry to the use of multivariate approaches, preventing more widespread application in the community. To the neuroscientist becoming familiar with multivariate analysis techniques, an initial survey of the field might present a bewildering variety of approaches that, although algorithmically similar, are presented with different emphases, typically by people with mathematics backgrounds. We believe that multivariate analysis techniques have sufficient potential to warrant better dissemination. Researchers should be able to employ them in an informed and accessible manner. The current article is an attempt at a didactic introduction of multivariate techniques for the novice. A conceptual introduction is followed with a very simple application to a diagnostic

  3. Factor analysis of multivariate data

    Digital Repository Service at National Institute of Oceanography (India)

    Fernandes, A.A.; Mahadevan, R.

    A brief introduction to factor analysis is presented. A FORTRAN program, which can perform the Q-mode and R-mode factor analysis and the singular value decomposition of a given data matrix is presented in Appendix B. This computer program, uses...

  4. A MULTIVARIATE ANALYSIS OF CROATIAN COUNTIES ENTREPRENEURSHIP

    Directory of Open Access Journals (Sweden)

    Elza Jurun

    2012-12-01

    Full Text Available In the focus of this paper is a multivariate analysis of Croatian Counties entrepreneurship. Complete data base available by official statistic institutions at national and regional level is used. Modern econometric methodology starting from a comparative analysis via multiple regression to multivariate cluster analysis is carried out as well as the analysis of successful or inefficacious entrepreneurship measured by indicators of efficiency, profitability and productivity. Time horizons of the comparative analysis are in 2004 and 2010. Accelerators of socio-economic development - number of entrepreneur investors, investment in fixed assets and current assets ratio in multiple regression model are analytically filtered between twenty-six independent variables as variables of the dominant influence on GDP per capita in 2010 as dependent variable. Results of multivariate cluster analysis of twentyone Croatian Counties are interpreted also in the sense of three Croatian NUTS 2 regions according to European nomenclature of regional territorial division of Croatia.

  5. Analysis of multivariate social science data

    CERN Document Server

    Bartholomew, David J; Galbraith, Jane; Moustaki, Irini

    2008-01-01

    Drawing on the authors' varied experiences working and teaching in the field, Analysis of Multivariate Social Science Data, Second Editionenables a basic understanding of how to use key multivariate methods in the social sciences. With updates in every chapter, this edition expands its topics to include regression analysis, confirmatory factor analysis, structural equation models, and multilevel models. After emphasizing the summarization of data in the first several chapters, the authors focus on regression analysis. This chapter provides a link between the two halves of the book, signal

  6. Multivariate Longitudinal Analysis with Bivariate Correlation Test

    Science.gov (United States)

    Adjakossa, Eric Houngla; Sadissou, Ibrahim; Hounkonnou, Mahouton Norbert; Nuel, Gregory

    2016-01-01

    In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more general expressions of the model’s parameters estimators. These estimators can be used in the framework of the multivariate longitudinal data analysis as well as in the more general context of the analysis of multivariate multilevel data. By using a likelihood ratio test, we test the significance of the correlations between the random effects of two dependent variables of the model, in order to investigate whether or not it is useful to model these dependent variables jointly. Simulation studies are done to assess both the parameter recovery performance of the EM estimators and the power of the test. Using two empirical data sets which are of longitudinal multivariate type and multivariate multilevel type, respectively, the usefulness of the test is illustrated. PMID:27537692

  7. Multivariate analysis: A statistical approach for computations

    Science.gov (United States)

    Michu, Sachin; Kaushik, Vandana

    2014-10-01

    Multivariate analysis is a type of multivariate statistical approach commonly used in, automotive diagnosis, education evaluating clusters in finance etc and more recently in the health-related professions. The objective of the paper is to provide a detailed exploratory discussion about factor analysis (FA) in image retrieval method and correlation analysis (CA) of network traffic. Image retrieval methods aim to retrieve relevant images from a collected database, based on their content. The problem is made more difficult due to the high dimension of the variable space in which the images are represented. Multivariate correlation analysis proposes an anomaly detection and analysis method based on the correlation coefficient matrix. Anomaly behaviors in the network include the various attacks on the network like DDOs attacks and network scanning.

  8. Schmidt decomposition and multivariate statistical analysis

    Science.gov (United States)

    Bogdanov, Yu. I.; Bogdanova, N. A.; Fastovets, D. V.; Luckichev, V. F.

    2016-12-01

    The new method of multivariate data analysis based on the complements of classical probability distribution to quantum state and Schmidt decomposition is presented. We considered Schmidt formalism application to problems of statistical correlation analysis. Correlation of photons in the beam splitter output channels, when input photons statistics is given by compound Poisson distribution is examined. The developed formalism allows us to analyze multidimensional systems and we have obtained analytical formulas for Schmidt decomposition of multivariate Gaussian states. It is shown that mathematical tools of quantum mechanics can significantly improve the classical statistical analysis. The presented formalism is the natural approach for the analysis of both classical and quantum multivariate systems and can be applied in various tasks associated with research of dependences.

  9. Multivariate Analysis of Ladle Vibration

    Science.gov (United States)

    Yenus, Jaefer; Brooks, Geoffrey; Dunn, Michelle

    2016-08-01

    The homogeneity of composition and uniformity of temperature of the steel melt before it is transferred to the tundish are crucial in making high-quality steel product. The homogenization process is performed by stirring the melt using inert gas in ladles. Continuous monitoring of this process is important to make sure the action of stirring is constant throughout the ladle. Currently, the stirring process is monitored by process operators who largely rely on visual and acoustic phenomena from the ladle. However, due to lack of measurable signals, the accuracy and suitability of this manual monitoring are problematic. The actual flow of argon gas to the ladle may not be same as the flow gage reading due to leakage along the gas line components. As a result, the actual degree of stirring may not be correctly known. Various researchers have used one-dimensional vibration, and sound and image signals measured from the ladle to predict the degree of stirring inside. They developed online sensors which are indeed to monitor the online stirring phenomena. In this investigation, triaxial vibration signals have been measured from a cold water model which is a model of an industrial ladle. Three flow rate ranges and varying bath heights were used to collect vibration signals. The Fast Fourier Transform was applied to the dataset before it has been analyzed using principal component analysis (PCA) and partial least squares (PLS). PCA was used to unveil the structure in the experimental data. PLS was mainly applied to predict the stirring from the vibration response. It was found that for each flow rate range considered in this study, the informative signals reside in different frequency ranges. The first latent variables in these frequency ranges explain more than 95 pct of the variation in the stirring process for the entire single layer and the double layer data collected from the cold model. PLS analysis in these identified frequency ranges demonstrated that the latent

  10. Multivariate data analysis of 2 DE data

    DEFF Research Database (Denmark)

    Wulff, Tune; Jokumsen, Alfred; Jessen, Flemming

    achieved by 2-DE. Protein spots, which individually or in combination with other spots varied according to hypoxia were found by multivariate data analysis (partial least squares regression) on group scaled data (normalised spot volumes) followed by selection of significant spots by jack-knifing. Tandem...

  11. Multivariate Analysis of Industrial Scale Fermentation Data

    DEFF Research Database (Denmark)

    Mears, Lisa; Nørregård, Rasmus; Stocks, Stuart M.

    2015-01-01

    Multivariate analysis allows process understanding to be gained from the vast and complex datasets recorded from fermentation processes, however the application of such techniques to this field can be limited by the data pre-processing requirements and data handling. In this work many iterations...

  12. Multivariate data analysis of 2 DE data

    DEFF Research Database (Denmark)

    Wulff, Tune; Jokumsen, Alfred; Jessen, Flemming

    achieved by 2-DE. Protein spots, which individually or in combination with other spots varied according to hypoxia were found by multivariate data analysis (partial least squares regression) on group scaled data (normalised spot volumes) followed by selection of significant spots by jack-knifing. Tandem...

  13. Multivariate analysis: greater insights into complex systems

    Science.gov (United States)

    Many agronomic researchers measure and collect multiple response variables in an effort to understand the more complex nature of the system being studied. Multivariate (MV) statistical methods encompass the simultaneous analysis of all random variables (RV) measured on each experimental or sampling ...

  14. Approaches to Assessment in Multivariate Analysis.

    Science.gov (United States)

    O'Connell, Ann A.

    This paper reviews trends in assessment in quantitative courses and illustrates several options and approaches to assessment for advanced courses at the graduate level, especially in multivariate analysis. The paper provides a summary of how a researcher has used alternatives to traditional methods of assessment in a course on multivariate…

  15. Multivariate analysis of longitudinal rates of change.

    Science.gov (United States)

    Bryan, Matthew; Heagerty, Patrick J

    2016-12-10

    Longitudinal data allow direct comparison of the change in patient outcomes associated with treatment or exposure. Frequently, several longitudinal measures are collected that either reflect a common underlying health status, or characterize processes that are influenced in a similar way by covariates such as exposure or demographic characteristics. Statistical methods that can combine multivariate response variables into common measures of covariate effects have been proposed in the literature. Current methods for characterizing the relationship between covariates and the rate of change in multivariate outcomes are limited to select models. For example, 'accelerated time' methods have been developed which assume that covariates rescale time in longitudinal models for disease progression. In this manuscript, we detail an alternative multivariate model formulation that directly structures longitudinal rates of change and that permits a common covariate effect across multiple outcomes. We detail maximum likelihood estimation for a multivariate longitudinal mixed model. We show via asymptotic calculations the potential gain in power that may be achieved with a common analysis of multiple outcomes. We apply the proposed methods to the analysis of a trivariate outcome for infant growth and compare rates of change for HIV infected and uninfected infants. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  16. Multivariate analysis of endometrial tissue fluorescence spectra

    Science.gov (United States)

    Vaitkuviene, Aurelija; Auksorius, E.; Fuchs, D.; Gavriushin, V.

    2002-10-01

    Background and Objective: The detailed multivariate analysis of endometrial tissue fluorescence spectra was done. Spectra underlying features and classification algorithm were analyzed. An effort has been made to determine the importance of neopterin component in endometrial premalignization. Study Design/Materials and Methods: Biomedical tissue fluorescence was measured by excitation with the Nd YAG laser third harmonic. Multivariate analysis techniques were used to analyze fluorescence spectra. Biomedical optics group at Vilnius University analyzed the neopterin substance supplied by the Institute of Medical Chemistry and Biochemistry of Innsbruck University. Results: Seven statistically significant spectral compounds were found. The classification algorithm classifying samples to histopathological categories was developed and resulted in sensitivity of 80% and specificity 93% for malignant vs. hyperplastic and normal. Conclusions: Fluorescence spectra could be classified with high accuracy. Spectral variation underlying features can be extracted. Neopterin component might play an important role in endometrial hyperplasia development.

  17. Power Estimation in Multivariate Analysis of Variance

    Directory of Open Access Journals (Sweden)

    Jean François Allaire

    2007-09-01

    Full Text Available Power is often overlooked in designing multivariate studies for the simple reason that it is believed to be too complicated. In this paper, it is shown that power estimation in multivariate analysis of variance (MANOVA can be approximated using a F distribution for the three popular statistics (Hotelling-Lawley trace, Pillai-Bartlett trace, Wilk`s likelihood ratio. Consequently, the same procedure, as in any statistical test, can be used: computation of the critical F value, computation of the noncentral parameter (as a function of the effect size and finally estimation of power using a noncentral F distribution. Various numerical examples are provided which help to understand and to apply the method. Problems related to post hoc power estimation are discussed.

  18. Multivariate Analysis for the Processing of Signals

    Directory of Open Access Journals (Sweden)

    Beattie J.R.

    2014-01-01

    Full Text Available Real-world experiments are becoming increasingly more complex, needing techniques capable of tracking this complexity. Signal based measurements are often used to capture this complexity, where a signal is a record of a sample’s response to a parameter (e.g. time, displacement, voltage, wavelength that is varied over a range of values. In signals the responses at each value of the varied parameter are related to each other, depending on the composition or state sample being measured. Since signals contain multiple information points, they have rich information content but are generally complex to comprehend. Multivariate Analysis (MA has profoundly transformed their analysis by allowing gross simplification of the tangled web of variation. In addition MA has also provided the advantage of being much more robust to the influence of noise than univariate methods of analysis. In recent years, there has been a growing awareness that the nature of the multivariate methods allows exploitation of its benefits for purposes other than data analysis, such as pre-processing of signals with the aim of eliminating irrelevant variations prior to analysis of the signal of interest. It has been shown that exploiting multivariate data reduction in an appropriate way can allow high fidelity denoising (removal of irreproducible non-signals, consistent and reproducible noise-insensitive correction of baseline distortions (removal of reproducible non-signals, accurate elimination of interfering signals (removal of reproducible but unwanted signals and the standardisation of signal amplitude fluctuations. At present, the field is relatively small but the possibilities for much wider application are considerable. Where signal properties are suitable for MA (such as the signal being stationary along the x-axis, these signal based corrections have the potential to be highly reproducible, and highly adaptable and are applicable in situations where the data is noisy or

  19. COSIMA data analysis using multivariate techniques

    Directory of Open Access Journals (Sweden)

    J. Silén

    2014-08-01

    Full Text Available We describe how to use multivariate analysis of complex TOF-SIMS spectra introducing the method of random projections. The technique allows us to do full clustering and classification of the measured mass spectra. In this paper we use the tool for classification purposes. The presentation describes calibration experiments of 19 minerals on Ag and Au substrates using positive mode ion spectra. The discrimination between individual minerals gives a crossvalidation Cohen κ for classification of typically about 80%. We intend to use the method as a fast tool to deduce a qualitative similarity of measurements.

  20. International Conference on Measurement and Multivariate Analysis

    CERN Document Server

    Baba, Yasumasa; Bozdogan, Hamparsum; Kanefuji, Koji; Measurement and Multivariate Analysis

    2002-01-01

    Diversity is characteristic of the information age and also of statistics. To date, the social sciences have contributed greatly to the development of handling data under the rubric of measurement, while the statistical sciences have made phenomenal advances in theory and algorithms. Measurement and Multivariate Analysis promotes an effective interplay between those two realms of research-diversity with unity. The union and the intersection of those two areas of interest are reflected in the papers in this book, drawn from an international conference in Banff, Canada, with participants from 15 countries. In five major categories - scaling, structural analysis, statistical inference, algorithms, and data analysis - readers will find a rich variety of topics of current interest in the extended statistical community.

  1. Classification of adulterated honeys by multivariate analysis.

    Science.gov (United States)

    Amiry, Saber; Esmaiili, Mohsen; Alizadeh, Mohammad

    2017-06-01

    In this research, honey samples were adulterated with date syrup (DS) and invert sugar syrup (IS) at three concentrations (7%, 15% and 30%). 102 adulterated samples were prepared in six batches with 17 replications for each batch. For each sample, 32 parameters including color indices, rheological, physical, and chemical parameters were determined. To classify the samples, based on type and concentrations of adulterant, a multivariate analysis was applied using principal component analysis (PCA) followed by a linear discriminant analysis (LDA). Then, 21 principal components (PCs) were selected in five sets. Approximately two-thirds were identified correctly using color indices (62.75%) or rheological properties (67.65%). A power discrimination was obtained using physical properties (97.06%), and the best separations were achieved using two sets of chemical properties (set 1: lactone, diastase activity, sucrose - 100%) (set 2: free acidity, HMF, ash - 95%). Copyright © 2016 Elsevier Ltd. All rights reserved.

  2. Multivariate Analysis of Genotype-Phenotype Association.

    Science.gov (United States)

    Mitteroecker, Philipp; Cheverud, James M; Pavlicev, Mihaela

    2016-04-01

    With the advent of modern imaging and measurement technology, complex phenotypes are increasingly represented by large numbers of measurements, which may not bear biological meaning one by one. For such multivariate phenotypes, studying the pairwise associations between all measurements and all alleles is highly inefficient and prevents insight into the genetic pattern underlying the observed phenotypes. We present a new method for identifying patterns of allelic variation (genetic latent variables) that are maximally associated-in terms of effect size-with patterns of phenotypic variation (phenotypic latent variables). This multivariate genotype-phenotype mapping (MGP) separates phenotypic features under strong genetic control from less genetically determined features and thus permits an analysis of the multivariate structure of genotype-phenotype association, including its dimensionality and the clustering of genetic and phenotypic variables within this association. Different variants of MGP maximize different measures of genotype-phenotype association: genetic effect, genetic variance, or heritability. In an application to a mouse sample, scored for 353 SNPs and 11 phenotypic traits, the first dimension of genetic and phenotypic latent variables accounted for >70% of genetic variation present in all 11 measurements; 43% of variation in this phenotypic pattern was explained by the corresponding genetic latent variable. The first three dimensions together sufficed to account for almost 90% of genetic variation in the measurements and for all the interpretable genotype-phenotype association. Each dimension can be tested as a whole against the hypothesis of no association, thereby reducing the number of statistical tests from 7766 to 3-the maximal number of meaningful independent tests. Important alleles can be selected based on their effect size (additive or nonadditive effect on the phenotypic latent variable). This low dimensionality of the genotype-phenotype map

  3. Multivariate Analysis of Genotype–Phenotype Association

    Science.gov (United States)

    Mitteroecker, Philipp; Cheverud, James M.; Pavlicev, Mihaela

    2016-01-01

    With the advent of modern imaging and measurement technology, complex phenotypes are increasingly represented by large numbers of measurements, which may not bear biological meaning one by one. For such multivariate phenotypes, studying the pairwise associations between all measurements and all alleles is highly inefficient and prevents insight into the genetic pattern underlying the observed phenotypes. We present a new method for identifying patterns of allelic variation (genetic latent variables) that are maximally associated—in terms of effect size—with patterns of phenotypic variation (phenotypic latent variables). This multivariate genotype–phenotype mapping (MGP) separates phenotypic features under strong genetic control from less genetically determined features and thus permits an analysis of the multivariate structure of genotype–phenotype association, including its dimensionality and the clustering of genetic and phenotypic variables within this association. Different variants of MGP maximize different measures of genotype–phenotype association: genetic effect, genetic variance, or heritability. In an application to a mouse sample, scored for 353 SNPs and 11 phenotypic traits, the first dimension of genetic and phenotypic latent variables accounted for >70% of genetic variation present in all 11 measurements; 43% of variation in this phenotypic pattern was explained by the corresponding genetic latent variable. The first three dimensions together sufficed to account for almost 90% of genetic variation in the measurements and for all the interpretable genotype–phenotype association. Each dimension can be tested as a whole against the hypothesis of no association, thereby reducing the number of statistical tests from 7766 to 3—the maximal number of meaningful independent tests. Important alleles can be selected based on their effect size (additive or nonadditive effect on the phenotypic latent variable). This low dimensionality of the

  4. Multivariate analysis of data in sensory science

    CERN Document Server

    Naes, T; Risvik, E

    1996-01-01

    The state-of-the-art of multivariate analysis in sensory science is described in this volume. Both methods for aggregated and individual sensory profiles are discussed. Processes and results are presented in such a way that they can be understood not only by statisticians but also by experienced sensory panel leaders and users of sensory analysis. The techniques presented are focused on examples and interpretation rather than on the technical aspects, with an emphasis on new and important methods which are possibly not so well known to scientists in the field. Important features of the book are discussions on the relationship among the methods with a strong accent on the connection between problems and methods. All procedures presented are described in relation to sensory data and not as completely general statistical techniques. Sensory scientists, applied statisticians, chemometricians, those working in consumer science, food scientists and agronomers will find this book of value.

  5. Urban water quality evaluation using multivariate analysis

    Directory of Open Access Journals (Sweden)

    Petr Praus

    2007-06-01

    Full Text Available A data set, obtained for the sake of drinking water quality monitoring, was analysed by multivariate methods. Principal component analysis (PCA reduced the data dimensionality from 18 original physico-chemical and microbiological parameters determined in drinking water samples to 6 principal components explaining about 83 % of the data variability. These 6 components represented inorganic salts, nitrate/pH, iron, chlorine, nitrite/ammonium traces, and heterotrophic bacteria. Using the PCA scatter plot and the Ward's clustering of the samples characterized by the first and second principal components, three clusters were revealed. These clusters sorted drinking water samples according to their origin - ground and surface water. The PCA results were confirmed by the factor analysis and hierarchical clustering of the original data.

  6. Augmented Classical Least Squares Multivariate Spectral Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Haaland, David M. (Albuquerque, NM); Melgaard, David K. (Albuquerque, NM)

    2005-01-11

    A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.

  7. Augmented Classical Least Squares Multivariate Spectral Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Haaland, David M. (Albuquerque, NM); Melgaard, David K. (Albuquerque, NM)

    2005-07-26

    A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.

  8. Kaempferol inhibits cell proliferation and glycolysis in esophagus squamous cell carcinoma via targeting EGFR signaling pathway.

    Science.gov (United States)

    Yao, Shihua; Wang, Xiaowei; Li, Chunguang; Zhao, Tiejun; Jin, Hai; Fang, Wentao

    2016-08-01

    Antitumor activity of kaempferol has been studied in various tumor types, but its potency in esophagus squamous cell carcinoma is rarely known. Here, we reported the activity of kaempferol against esophagus squamous cell carcinoma as well as its antitumor mechanisms. Results of cell proliferation and colony formation assay showed that kaempferol substantially inhibited tumor cell proliferation and clone formation in vitro. Flow cytometric analysis demonstrated that tumor cells were induced G0/G1 phase arrest after kaempferol treatment, and the expression of protein involved in cell cycle regulation was dramatically changed. Except the potency on cell proliferation, we also discovered that kaempferol had a significant inhibitory effect against tumor glycolysis. With the downregulation of hexokinase-2, glucose uptake and lactate production in tumor cells were dramatically declined. Mechanism studies revealed kaempferol had a direct effect on epidermal growth factor receptor (EGFR) activity, and along with the inhibition of EGFR, its downstream signaling pathways were also markedly suppressed. Further investigations found that exogenous overexpression of EGFR in tumor cells substantially attenuated glycolysis suppression induced by kaempferol, which implied that EGFR also played an important role in kaempferol-mediated glycolysis inhibition. Finally, the antitumor activity of kaempferol was validated in xenograft model and kaempferol prominently restrained tumor growth in vivo. Meanwhile, dramatic decrease of EGFR activity and hexokinase-2 expression were observed in kaempferol-treated tumor tissue, which confirmed these findings in vitro. Briefly, these studies suggested that kaempferol, or its analogues, may serve as effective candidates for esophagus squamous cell carcinoma management.

  9. Multivariate analysis applied to tomato hybrid production.

    Science.gov (United States)

    Balasch, S; Nuez, F; Palomares, G; Cuartero, J

    1984-11-01

    Twenty characters were measured on 60 tomato varieties cultivated in the open-air and in polyethylene plastic-house. Data were analyzed by means of principal components, factorial discriminant methods, Mahalanobis D(2) distances and principal coordinate techniques. Factorial discriminant and Mahalanobis D(2) distances methods, both of which require collecting data plant by plant, lead to similar conclusions as the principal components method that only requires taking data by plots. Characters that make up the principal components in both environments studied are the same, although the relative importance of each one of them varies within the principal components. By combining information supplied by multivariate analysis with the inheritance mode of characters, crossings among cultivars can be experimented with that will produce heterotic hybrids showing characters within previously established limits.

  10. Hierarchical multivariate covariance analysis of metabolic connectivity.

    Science.gov (United States)

    Carbonell, Felix; Charil, Arnaud; Zijdenbos, Alex P; Evans, Alan C; Bedell, Barry J

    2014-12-01

    Conventional brain connectivity analysis is typically based on the assessment of interregional correlations. Given that correlation coefficients are derived from both covariance and variance, group differences in covariance may be obscured by differences in the variance terms. To facilitate a comprehensive assessment of connectivity, we propose a unified statistical framework that interrogates the individual terms of the correlation coefficient. We have evaluated the utility of this method for metabolic connectivity analysis using [18F]2-fluoro-2-deoxyglucose (FDG) positron emission tomography (PET) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. As an illustrative example of the utility of this approach, we examined metabolic connectivity in angular gyrus and precuneus seed regions of mild cognitive impairment (MCI) subjects with low and high β-amyloid burdens. This new multivariate method allowed us to identify alterations in the metabolic connectome, which would not have been detected using classic seed-based correlation analysis. Ultimately, this novel approach should be extensible to brain network analysis and broadly applicable to other imaging modalities, such as functional magnetic resonance imaging (MRI).

  11. Radiosensitization of non-small cell lung cancer by kaempferol.

    Science.gov (United States)

    Kuo, Wei-Ting; Tsai, Yuan-Chung; Wu, His-Chin; Ho, Yung-Jen; Chen, Yueh-Sheng; Yao, Chen-Han; Yao, Chun-Hsu

    2015-11-01

    The aim of the present study was to determine whether kaempferol has a radiosensitization potential for lung cancer in vitro and in vivo. The in vitro radio-sensitization activity of kaempferol was elucidated in A-549 lung cancer cells by using an MTT (3-(4 5-dimethylthiazol-2-yl)-25-diphenyl-tetrazolium bromide) assay, cell cycle analysis and clonogenic assay. The in vivo activity was evaluated in the BALB/c nude mouse xenograft model of A-549 cells by hematoxylin and eosin staining and immunohistochemistry, and the tumor volume was recorded. Protein levels of the apoptotic pathway were detected by western blot analysis. Treatment with kaempferol inhibited the growth of A-549 cells through activation of apoptotic pathway. However, the same doses did not affect HFL1 normal lung cell growth. Kaempferol induced G2/M cell cycle arrest and the enhancement of radiation-induced death and clonogenic survival inhibition. The in vivo data showed that kaempferol increased tumor cell apoptosis and killing of radiation. In conclusion, the findings demonstrated that kaempferol increased tumor cell killing by radiation in vitro and in vivo through inhibition of the AKT/PI3K and ERK pathways and activation of the mitochondria apoptosis pathway. The results of the present study provided solid evidence that kaempferol is a safe and potential radiosensitizer.

  12. Kaempferol inhibits Entamoeba histolytica growth by altering cytoskeletal functions.

    Science.gov (United States)

    Bolaños, Verónica; Díaz-Martínez, Alfredo; Soto, Jacqueline; Marchat, Laurence A; Sanchez-Monroy, Virginia; Ramírez-Moreno, Esther

    2015-11-01

    The flavonoid kaempferol obtained from Helianthemum glomeratum, an endemic Mexican medicinal herb used to treat gastrointestinal disorders, has been shown to inhibit growth of Entamoeba histolytica trophozoites in vitro; however, the mechanisms associated with this activity have not been documented. Several works reported that kaempferol affects cytoskeleton in mammalian cells. In order to gain insights into the action mechanisms involved in the anti-amoebic effect of kaempferol, here we evaluated the effect of this compound on the pathogenic events driven by the cytoskeleton during E. histolytica infection. We also carried out a two dimensional gel-based proteomic analysis to evidence modulated proteins that could explain the phenotypical changes observed in trophozoites. Our results showed that kaempferol produces a dose-dependent effect on trophozoites growth and viability with optimal concentration being 27.7 μM. Kaempferol also decreased adhesion, it increased migration and phagocytic activity, but it did not affect erythrocyte binding nor cytolytic capacity of E. histolytica. Congruently, proteomic analysis revealed that the cytoskeleton proteins actin, myosin II heavy chain and cortexillin II were up-regulated in response to kaempferol treatment. In conclusion, kaempferol anti-amoebic effects were associated with deregulation of proteins related with cytoskeleton, which altered invasion mechanisms.

  13. Multivariate statistical analysis of wildfires in Portugal

    Science.gov (United States)

    Costa, Ricardo; Caramelo, Liliana; Pereira, Mário

    2013-04-01

    Several studies demonstrate that wildfires in Portugal present high temporal and spatial variability as well as cluster behavior (Pereira et al., 2005, 2011). This study aims to contribute to the characterization of the fire regime in Portugal with the multivariate statistical analysis of the time series of number of fires and area burned in Portugal during the 1980 - 2009 period. The data used in the analysis is an extended version of the Rural Fire Portuguese Database (PRFD) (Pereira et al, 2011), provided by the National Forest Authority (Autoridade Florestal Nacional, AFN), the Portuguese Forest Service, which includes information for more than 500,000 fire records. There are many multiple advanced techniques for examining the relationships among multiple time series at the same time (e.g., canonical correlation analysis, principal components analysis, factor analysis, path analysis, multiple analyses of variance, clustering systems). This study compares and discusses the results obtained with these different techniques. Pereira, M.G., Trigo, R.M., DaCamara, C.C., Pereira, J.M.C., Leite, S.M., 2005: "Synoptic patterns associated with large summer forest fires in Portugal". Agricultural and Forest Meteorology. 129, 11-25. Pereira, M. G., Malamud, B. D., Trigo, R. M., and Alves, P. I.: The history and characteristics of the 1980-2005 Portuguese rural fire database, Nat. Hazards Earth Syst. Sci., 11, 3343-3358, doi:10.5194/nhess-11-3343-2011, 2011 This work is supported by European Union Funds (FEDER/COMPETE - Operational Competitiveness Programme) and by national funds (FCT - Portuguese Foundation for Science and Technology) under the project FCOMP-01-0124-FEDER-022692, the project FLAIR (PTDC/AAC-AMB/104702/2008) and the EU 7th Framework Program through FUME (contract number 243888).

  14. Method for statistical data analysis of multivariate observations

    CERN Document Server

    Gnanadesikan, R

    1997-01-01

    A practical guide for multivariate statistical techniques-- now updated and revised In recent years, innovations in computer technology and statistical methodologies have dramatically altered the landscape of multivariate data analysis. This new edition of Methods for Statistical Data Analysis of Multivariate Observations explores current multivariate concepts and techniques while retaining the same practical focus of its predecessor. It integrates methods and data-based interpretations relevant to multivariate analysis in a way that addresses real-world problems arising in many areas of inte

  15. Kaempferol triosides from Silphium perfoliatum.

    Science.gov (United States)

    el-Sayed, Nabil H; Wojcińska, Małgorzata; Drost-Karbowska, Krystyna; Matławska, Irena; Williams, Jeffrey; Mabry, Tom J

    2002-08-01

    Two apiose-containing kaempferol triosides, together with nine known flavonoids were isolated from the leaves of Silphium perfoliatum L. Their structures were elucidated by acid hydrolysis and spectroscopic methods including UV, LSI MS, FAB MS, CI MS, (1)H, (13)C and 2D-NMR, DEPT, HMQC and HMBC experiments. The two new compounds were identified as kaempferol 3-O-beta-D-apiofuranoside 7-O-alpha-L-rhamnosyl-(1"-->6"')-O-beta-D-galactopyranoside and kaempferol 3-O-beta-D-apiofuranoside 7-O-alpha-L-rhamnosyl-(1''''--> 6"')-O-beta-D (2"'-O-E-caffeoylgalactopyranoside).

  16. An Introduction to Applied Multivariate Analysis

    CERN Document Server

    Raykov, Tenko

    2008-01-01

    Focuses on the core multivariate statistics topics which are of fundamental relevance for its understanding. This book emphasis on the topics that are critical to those in the behavioral, social, and educational sciences.

  17. Multivariate analysis of industrial scale fermentation data

    DEFF Research Database (Denmark)

    Mears, Lisa; Nørregård, Rasmus; Stocks, Stuart;

    , and thereforeareas offocus for optimising the processoperation.This requires multivariate methods which canutilise the complexdatasetswhich areroutinely collected, containing online measured variables and offline sample data.Fermentation processes are highly sensitive to operational changes, as well as between...

  18. Multivariate Model for Test Response Analysis

    NARCIS (Netherlands)

    Krishnan, Shaji; Krishnan, Shaji; Kerkhoff, Hans G.

    2010-01-01

    A systematic approach to construct an effective multivariate test response model for capturing manufacturing defects in electronic products is described. The effectiveness of the model is demonstrated by its capability in reducing the number of test-points, while achieving the maximal coverage

  19. Multivariate model for test response analysis

    NARCIS (Netherlands)

    Krishnan, S.; Kerkhoff, H.G.

    2010-01-01

    A systematic approach to construct an effective multivariate test response model for capturing manufacturing defects in electronic products is described. The effectiveness of the model is demonstrated by its capability in reducing the number of test-points, while achieving the maximal coverage attai

  20. Multivariate model for test response analysis

    NARCIS (Netherlands)

    Krishnan, S.; Kerkhoff, H.G.

    2010-01-01

    A systematic approach to construct an effective multivariate test response model for capturing manufacturing defects in electronic products is described. The effectiveness of the model is demonstrated by its capability in reducing the number of test-points, while achieving the maximal coverage attai

  1. Kernel Multivariate Analysis Framework for Supervised Subspace Learning: A Tutorial on Linear and Kernel Multivariate Methods

    DEFF Research Database (Denmark)

    Arenas-Garcia, J.; Petersen, K.; Camps-Valls, G.

    2013-01-01

    sources become more common. A plethora of feature extraction methods are available in the literature collectively grouped under the field of multivariate analysis (MVA). This article provides a uniform treatment of several methods: principal component analysis (PCA), partial least squares (PLS), canonical...

  2. Kaempferol ameliorates symptoms of metabolic syndrome by regulating activities of liver X receptor-β.

    Science.gov (United States)

    Hoang, Minh-Hien; Jia, Yaoyao; Mok, Boram; Jun, Hee-jin; Hwang, Kwang-Yeon; Lee, Sung-Joon

    2015-08-01

    Kaempferol is a dietary flavonol previously shown to regulate cellular lipid and glucose metabolism. However, its molecular mechanisms of action and target proteins have remained elusive, probably due to the involvement of multiple proteins. This study investigated the molecular targets of kaempferol. Ligand binding of kaempferol to liver X receptors (LXRs) was quantified by time-resolved fluorescence resonance energy transfer and surface plasmon resonance analyses. Kaempferol directly binds to and induces the transactivation of LXRs, with stronger specificity for the β-subtype (EC50 = 0.33 μM). The oral administration of kaempferol in apolipoprotein-E-deficient mice (150 mg/day/kg body weight) significantly reduced plasma glucose and increased high-density lipoprotein cholesterol levels and insulin sensitivity compared with the vehicle-fed control. Kaempferol also reduced plasma triglyceride concentrations and did not cause liver steatosis, a common side effect of potent LXR activation. In immunoblotting analysis, kaempferol reduced the nuclear accumulation of sterol regulatory element-binding protein-1 (SREBP-1). Our results show that the suppression of SREBP-1 activity and the selectivity for LXR-β over LXR-α by kaempferol contribute to the reductions of plasma and hepatic triglyceride concentrations in mice fed kaempferol. They also suggest that kaempferol activates LXR-β and suppresses SREBP-1 to enhance symptoms in metabolic syndrome.

  3. The toolkit for multivariate data analysis TMVA 4

    CERN Document Server

    Speckmayer, P; Stelzer, J; Voss, H

    2010-01-01

    The toolkit for multivariate analysis, TMVA, provides a large set of advanced multivariate analysis techniques for signal/background classification. In addition, TMVA now also contains regression analysis, all embedded in a framework capable of handling the preprocessing of the data and the evaluation of the output, thus allowing a simple and convenient use of multivariate techniques. The analysis techniques implemented in TMVA can be invoked easily and the direct comparison of their performance allows the user to choose the most appropriate for a particular data analysis. This article gives an overview of the TMVA package and presents recently developed features.

  4. Introduction to multivariate analysis linear and nonlinear modeling

    CERN Document Server

    Konishi, Sadanori

    2014-01-01

    ""The presentation is always clear and several examples and figures facilitate an easy understanding of all the techniques. The book can be used as a textbook in advanced undergraduate courses in multivariate analysis, and can represent a valuable reference manual for biologists and engineers working with multivariate datasets.""-Fabio Rapallo, Zentralblatt MATH 1296

  5. Exploratory Tobit factor analysis for multivariate censored data

    NARCIS (Netherlands)

    Kamakura, WA; Wedel, M

    2001-01-01

    We propose Multivariate Tobit models with a factor structure on the covariance matrix. Such models are particularly useful in the exploratory analysis of multivariate censored data and the identification of latent variables from behavioral data. The factor structure provides a parsimonious

  6. Astrocladistics: Multivariate Evolutionary Analysis in Astrophysics

    CERN Document Server

    Fraix-Burnet, Didier

    2010-01-01

    The Hubble tuning fork diagram, based on morphology and established in the 1930s, has always been the preferred scheme for classification of galaxies. However, the current large amount of data up to higher and higher redshifts asks for more sophisticated statistical approaches like multivariate analyses. Clustering analyses are still very confidential, and do not take into account the unavoidable characteristics in our Universe: evolution. Assuming branching evolution of galaxies as a 'transmission with modification', we have shown that the concepts and tools of phylogenetic systematics (cladistics) can be heuristically transposed to the case of galaxies. This approach that we call "astrocladistics", has now successfully been applied on several samples of galaxies and globular clusters. Maximum parsimony and distance-based approaches are the most popular methods to produce phylogenetic trees and, like most other studies, we had to discretize our variables. However, since astrophysical data are intrinsically c...

  7. Kaempferol inhibits cancer cell growth by antagonizing estrogen-related receptor α and γ activities.

    Science.gov (United States)

    Wang, Haibin; Gao, Minghui; Wang, Junjian

    2013-11-01

    Kaempferol is a dietary flavonoid that can function as a selective estrogen receptor modulator (SERM). Estrogen-related receptors alpha and gamma (ERRα and ERRγ) are orphan nuclear receptors that play important roles in mitochondrial biogenesis and cancer development. We have shown that kaempferol can functionally antagonize the activities of ERRs based on both response element reporter systems and target gene analysis. Kaempferol modulation of mitochondrial function and suppression cancer cell growth has been confirmed. These findings suggest that kaempferol may exert their anti-cancer activities through antagonizing ERRs activities.

  8. Fear of Crime in the United States: A Multivariate Analysis

    Science.gov (United States)

    Clemente, Frank; Kleiman, Michael B.

    1977-01-01

    Multivariate Nominal Scale Analysis (MNA) was used to assess the independent ability of each variable to predict respondents who indicated a fear of crime (42 percent) and those who did not (58 percent). (Author/AM)

  9. Multivariate Time Series Analysis for Optimum Production Forecast ...

    African Journals Online (AJOL)

    FIRST LADY

    Keywords: production model, inventory management, multivariate time series ... regard when companies over stock raw materials inventory as a result of .... Error Analysis for Forecasts of 2008-2014 to Establish Model out of. Control.

  10. Matrix-based introduction to multivariate data analysis

    CERN Document Server

    Adachi, Kohei

    2016-01-01

    This book enables readers who may not be familiar with matrices to understand a variety of multivariate analysis procedures in matrix forms. Another feature of the book is that it emphasizes what model underlies a procedure and what objective function is optimized for fitting the model to data. The author believes that the matrix-based learning of such models and objective functions is the fastest way to comprehend multivariate data analysis. The text is arranged so that readers can intuitively capture the purposes for which multivariate analysis procedures are utilized: plain explanations of the purposes with numerical examples precede mathematical descriptions in almost every chapter. This volume is appropriate for undergraduate students who already have studied introductory statistics. Graduate students and researchers who are not familiar with matrix-intensive formulations of multivariate data analysis will also find the book useful, as it is based on modern matrix formulations with a special emphasis on ...

  11. Multivariate analysis of 2-DE protein patterns - Practical approaches

    DEFF Research Database (Denmark)

    Jacobsen, Charlotte; Jacobsen, Susanne; Grove, H.

    2007-01-01

    Practical approaches to the use of multivariate data analysis of 2-DE protein patterns are demonstrated by three independent strategies for the image analysis and the multivariate analysis on the same set of 2-DE data. Four wheat varieties were selected on the basis of their baking quality. Two...... of the varieties were of strong baking quality and hard wheat kernel and two were of weak baking quality and soft kernel. Gliadins at different stages of grain development were analyzed by the application of multivariate data analysis on images of 2-DEs. Patterns related to the wheat varieties, harvest times...... and quality were detected on images of 2-DE protein patterns for all the three strategies. The use of the multivariate methods was evaluated in the alignment and matching procedures of 2-DE gels. All the three strategies were able to discriminate the samples according to quality, harvest time and variety...

  12. Synthesis, characterization and anticancer activity of kaempferol-zinc(II) complex.

    Science.gov (United States)

    Tu, Lv-Ying; Pi, Jiang; Jin, Hua; Cai, Ji-Ye; Deng, Sui-Ping

    2016-06-01

    According to the previous studies, the anticancer activity of flavonoids could be enhanced when they are coordinated with transition metal ions. In this work, kaempferol-zinc(II) complex (kaempferol-Zn) was synthesized and its chemical properties were characterized by UV-VIS, FT-IR, (1)H NMR, elemental analysis, electrospray mass spectrometry (ES-MS) and fluorescence spectroscopy, which showed that the synthesized complex was coordinated with a Zn(II) ion via the 3-OH and 4-oxo groups. The anticancer effects of kaempferol-Zn and free kaempferol on human oesophageal cancer cell line (EC9706) were compared. MTT results demonstrated that the killing effect of kaempferol-Zn was two times higher than that of free kaempferol. Atomic force microscopy (AFM) showed the morphological and ultrastructural changes of cellular membrane induced by kaempferol-Zn at subcellular or nanometer level. Moreover, flow cytometric analysis indicated that kaempferol-Zn could induce apoptosis in EC9706 cells by regulating intracellular calcium ions. Collectively, all the data showed that kaempferol-Zn might be served as a kind of potential anticancer agent.

  13. Classification Techniques for Multivariate Data Analysis.

    Science.gov (United States)

    1980-03-28

    analysis among biologists, botanists, and ecologists, while some social scientists may refer "typology". Other frequently encountered terms are pattern...the determinantal equation: lB -XW 0 (42) 49 The solutions X. are the eigenvalues of the matrix W-1 B 1 as in discriminant analysis. There are t non...Statistical Package for Social Sciences (SPSS) (14) subprogram FACTOR was used for the principal components analysis. It is designed both for the factor

  14. Kaempferol suppresses bladder cancer tumor growth by inhibiting cell proliferation and inducing apoptosis.

    Science.gov (United States)

    Dang, Qiang; Song, Wenbin; Xu, Defeng; Ma, Yanmin; Li, Feng; Zeng, Jin; Zhu, Guodong; Wang, Xinyang; Chang, Luke S; He, Dalin; Li, Lei

    2015-09-01

    The effects of the flavonoid compound, kaempferol, which is an inhibitor of cancer cell proliferation and an inducer of cell apoptosis have been shown in various cancers, including lung, pancreatic, and ovarian, but its effect has never been studied in bladder cancer. Here, we investigated the effects of kaempferol on bladder cancer using multiple in vitro cell lines and in vivo mice studies. The MTT assay results on various bladder cancer cell lines showed that kaempferol enhanced bladder cancer cell cytotoxicity. In contrast, when analyzed by the flow cytometric analysis, DNA ladder experiment, and TUNEL assay, kaempferol significantly was shown to induce apoptosis and cell cycle arrest. These in vitro results were confirmed in in vivo mice studies using subcutaneous xenografted mouse models. Consistent with the in vitro results, we found that treating mice with kaempferol significant suppression in tumor growth compared to the control group mice. Tumor tissue staining results showed decreased expressions of the growth related markers, yet increased expressions in apoptosis markers in the kaempferol treated group mice tissues compared to the control group mice. In addition, our in vitro and in vivo data showed kaempferol can also inhibit bladder cancer invasion and metastasis. Further mechanism dissection studies showed that significant down-regulation of the c-Met/p38 signaling pathway is responsible for the kaempferol mediated cell proliferation inhibition. All these findings suggest kaempferol might be an effective and novel chemotherapeutic drug to apply for the future therapeutic agent to combat bladder cancer.

  15. Outliers detection in multivariate time series by independent component analysis.

    Science.gov (United States)

    Baragona, Roberto; Battaglia, Francesco

    2007-07-01

    In multivariate time series, outlying data may be often observed that do not fit the common pattern. Occurrences of outliers are unpredictable events that may severely distort the analysis of the multivariate time series. For instance, model building, seasonality assessment, and forecasting may be seriously affected by undetected outliers. The structure dependence of the multivariate time series gives rise to the well-known smearing and masking phenomena that prevent using most outliers' identification techniques. It may be noticed, however, that a convenient way for representing multiple outliers consists of superimposing a deterministic disturbance to a gaussian multivariate time series. Then outliers may be modeled as nongaussian time series components. Independent component analysis is a recently developed tool that is likely to be able to extract possible outlier patterns. In practice, independent component analysis may be used to analyze multivariate observable time series and separate regular and outlying unobservable components. In the factor models framework too, it is shown that independent component analysis is a useful tool for detection of outliers in multivariate time series. Some algorithms that perform independent component analysis are compared. It has been found that all algorithms are effective in detecting various types of outliers, such as patches, level shifts, and isolated outliers, even at the beginning or the end of the stretch of observations. Also, there is no appreciable difference in the ability of different algorithms to display the outlying observations pattern.

  16. TMVA - Tool-kit for Multivariate Data Analysis in ROOT

    Energy Technology Data Exchange (ETDEWEB)

    Therhaag, Jan; Von Toerne, Eckhard [Univ. Bonn, Physikalisches Institut, Nussallee 12, 53115 Bonn (Germany); Hoecker, Andreas; Speckmayer, Peter [European Organization for Nuclear Research - CERN, CH-1211 Geneve 23 (Switzerland); Stelzer, Joerg [Deutsches Elektronen-Synchrotron - DESY, Platanenallee 6, D-15738 Zeuthen (Germany); Voss, Helge [Max-Planck-Institut fuer Kernphysik - MPI, Postfach 10 39 80, Saupfercheckweg 1, DE-69117 Heidelberg (Germany)

    2010-07-01

    Given the ever-increasing complexity of modern HEP data analysis, multivariate analysis techniques have proven an indispensable tool in extracting the most valuable information from the data. TMVA, the Tool-kit for Multivariate Data Analysis, provides a large variety of advanced multivariate analysis techniques for both signal/background classification and regression problems. In TMVA, all methods are embedded in a user-friendly framework capable of handling the pre-processing of the data as well as the evaluation of the results, thus allowing for a simple use of even the most sophisticated multivariate techniques. Convenient assessment and comparison of different analysis techniques enable the user to choose the most efficient approach for any particular data analysis task. TMVA is an integral part of the ROOT data analysis framework and is widely-used in the LHC experiments. In this talk I will review recent developments in TMVA, discuss typical use-cases in HEP and present the performance of our most important multivariate techniques on example data by comparing it to theoretical performance limits. (authors)

  17. Looking Back at the Gifi System of Nonlinear Multivariate Analysis

    Directory of Open Access Journals (Sweden)

    Peter G. M. van der Heijden

    2016-09-01

    Full Text Available Gifi was the nom de plume for a group of researchers led by Jan de Leeuw at the University of Leiden. Between 1970 and 1990 the group produced a stream of theoretical papers and computer programs in the area of nonlinear multivariate analysis that were very innovative. In an informal way this paper discusses the so-called Gifi system of nonlinear multivariate analysis, that entails homogeneity analysis (which is closely related to multiple correspondence analysis and generalizations. The history is discussed, giving attention to the scientific philosophy of this group, and links to machine learning are indicated.

  18. Multivariate analysis of TLD orientation effects

    Energy Technology Data Exchange (ETDEWEB)

    Archer, B.R.; Bushong, S.C.; Thornby, J.I.

    1980-07-01

    The effect of orientation on extruded thermoluminescent dosimeters has been investigated. TLD's placed on the surface and within a phantom were exposed separately to five diagnostic beam qualities and to /sup 60/Co ..gamma.. rays. The resulting data were subjected to analysis of variance and examined for significant correlations. The response of dosimeters on the phantom surface varied with orientation and was energy dependent. In the phantom and with /sup 60/Co, no orientation effects were observed.

  19. EURO AREA FISCAL STRUCTURES. A MULTIVARIATE ANALYSIS

    Directory of Open Access Journals (Sweden)

    HURDUZEU Gheorghe

    2014-07-01

    taxes on income of corporations and taxes on income of individuals and households and other current taxes. Actual social contributions were also split into employer’s actual contributions, employee’s social contributions and social contributions of self- and non-employed persons. As the primary data analysis revealed many differences between Euro Area member states, but also similarities concerning various fiscal aggregates, we completed the analysis through multidimensional analysis, with the aims of classifying Euro Area member states into subgroups with similar fiscal structures. Taking into consideration the above mentioned variables, we used cluster analysis in order to determine which member states have similar fiscal structures and which are the main similarities that characterize Euro Area in this respect.

  20. Multivariate Analysis of Solar Spectral Irradiance Measurements

    Science.gov (United States)

    Pilewskie, P.; Rabbette, M.

    2001-01-01

    Principal component analysis is used to characterize approximately 7000 downwelling solar irradiance spectra retrieved at the Southern Great Plains site during an Atmospheric Radiation Measurement (ARM) shortwave intensive operating period. This analysis technique has proven to be very effective in reducing a large set of variables into a much smaller set of independent variables while retaining the information content. It is used to determine the minimum number of parameters necessary to characterize atmospheric spectral irradiance or the dimensionality of atmospheric variability. It was found that well over 99% of the spectral information was contained in the first six mutually orthogonal linear combinations of the observed variables (flux at various wavelengths). Rotation of the principal components was effective in separating various components by their independent physical influences. The majority of the variability in the downwelling solar irradiance (380-1000 nm) was explained by the following fundamental atmospheric parameters (in order of their importance): cloud scattering, water vapor absorption, molecular scattering, and ozone absorption. In contrast to what has been proposed as a resolution to a clear-sky absorption anomaly, no unexpected gaseous absorption signature was found in any of the significant components.

  1. Virulence of Bacillus cereus: a multivariate analysis.

    Science.gov (United States)

    Minnaard, J; Delfederico, L; Vasseur, V; Hollmann, A; Rolny, I; Semorile, L; Pérez, P F

    2007-05-10

    Biological activity and presence of DNA sequences related to virulence genes were studied in 21 strains of the Bacillus cereus group. The activity of spent culture supernatants and the effect of infection by vegetative bacterial cells were assessed on cultured human enterocytes (Caco-2 cells). The effect of extracellular factors on the detachment, necrosis and mitochondrial dehydrogenase activity of cultured human enterocytes was studied. Hemolytic activity on rabbit red blood cells was also evaluated and the effect of direct procaryotic-eucaryotic interactions was assessed in infection assays with vegetative bacterial cells. Concerning virulence genes, presence of the DNA sequences corresponding to the genes entS, entFM, nhe (A, B and C), sph, hbl (A, B, C and D), piplC and bceT was assessed by PCR. Ribopatterns were determined by an automated riboprinting analysis after digestion of the DNA with EcoRI. Principal component analysis and biplots were used to address the relationship between variables. Results showed a wide range of biological activities: decrease in mitochondrial dehydrogenase activity, necrosis, cell detachment and hemolytic activity. These effects were strain-dependent. Concerning the occurrence of the DNA sequences tested, different patterns were found. In addition, ribotyping showed that strains under study grouped into two main clusters. One of these clusters includes all the strains that were positive for all the DNA sequences tested. Positive and negative correlations between variables under study were evidenced. Interestingly, high detaching strains were positively correlated with the presence of the sequences entS, nheC and sph. Within gene complexes, high correlation was found between sequences of the hbl complex. In contrast, sequences of the nhe complex were not correlated. Some strains clustered together in the biplots. These strains were positive for all the DNA sequences tested and they were able to detach enterocytes upon infection

  2. Multivariate Volatility Impulse Response Analysis of GFC News Events

    NARCIS (Netherlands)

    D.E. Allen (David); M.J. McAleer (Michael); R.J. Powell (Robert); A.K. Singh (Abhay)

    2015-01-01

    textabstractThis paper applies the Hafner and Herwartz (2006) (hereafter HH) approach to the analysis of multivariate GARCH models using volatility impulse response analysis. The data set features ten years of daily returns series for the New York Stock Exchange Index and the FTSE 100 index from the

  3. Multivariate Volatility Impulse Response Analysis of GFC News Events

    NARCIS (Netherlands)

    D.E. Allen (David); M.J. McAleer (Michael); R.J. Powell (Robert)

    2015-01-01

    markdownabstract__Abstract__ This paper applies the Hafner and Herwartz (2006) (hereafter HH) approach to the analysis of multivariate GARCH models using volatility impulse response analysis. The data set features ten years of daily returns series for the New York Stock Exchange Index and the FTSE

  4. Multivariate Volatility Impulse Response Analysis of GFC News Events

    NARCIS (Netherlands)

    D.E. Allen (David); M.J. McAleer (Michael); R.J. Powell (Robert)

    2015-01-01

    markdownabstract__Abstract__ This paper applies the Hafner and Herwartz (2006) (hereafter HH) approach to the analysis of multivariate GARCH models using volatility impulse response analysis. The data set features ten years of daily returns series for the New York Stock Exchange Index and the

  5. Multivariate Volatility Impulse Response Analysis of GFC News Events

    NARCIS (Netherlands)

    D.E. Allen (David); M.J. McAleer (Michael); R.J. Powell (Robert); A.K. Singh (Abhay)

    2015-01-01

    textabstractThis paper applies the Hafner and Herwartz (2006) (hereafter HH) approach to the analysis of multivariate GARCH models using volatility impulse response analysis. The data set features ten years of daily returns series for the New York Stock Exchange Index and the FTSE 100 index from the

  6. Association Analysis for Visual Exploration of Multivariate Scientific Data Sets.

    Science.gov (United States)

    Liu, Xiaotong; Shen, Han-Wei

    2016-01-01

    The heterogeneity and complexity of multivariate characteristics poses a unique challenge to visual exploration of multivariate scientific data sets, as it requires investigating the usually hidden associations between different variables and specific scalar values to understand the data's multi-faceted properties. In this paper, we present a novel association analysis method that guides visual exploration of scalar-level associations in the multivariate context. We model the directional interactions between scalars of different variables as information flows based on association rules. We introduce the concepts of informativeness and uniqueness to describe how information flows between scalars of different variables and how they are associated with each other in the multivariate domain. Based on scalar-level associations represented by a probabilistic association graph, we propose the Multi-Scalar Informativeness-Uniqueness (MSIU) algorithm to evaluate the informativeness and uniqueness of scalars. We present an exploration framework with multiple interactive views to explore the scalars of interest with confident associations in the multivariate spatial domain, and provide guidelines for visual exploration using our framework. We demonstrate the effectiveness and usefulness of our approach through case studies using three representative multivariate scientific data sets.

  7. Multivariate Meta-Analysis Using Individual Participant Data

    Science.gov (United States)

    Riley, R. D.; Price, M. J.; Jackson, D.; Wardle, M.; Gueyffier, F.; Wang, J.; Staessen, J. A.; White, I. R.

    2015-01-01

    When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is…

  8. Evaluation of Meterorite Amono Acid Analysis Data Using Multivariate Techniques

    Science.gov (United States)

    McDonald, G.; Storrie-Lombardi, M.; Nealson, K.

    1999-01-01

    The amino acid distributions in the Murchison carbonaceous chondrite, Mars meteorite ALH84001, and ice from the Allan Hills region of Antarctica are shown, using a multivariate technique known as Principal Component Analysis (PCA), to be statistically distinct from the average amino acid compostion of 101 terrestrial protein superfamilies.

  9. Robust methods for multivariate data analysis A1

    DEFF Research Database (Denmark)

    Frosch, Stina; Von Frese, J.; Bro, Rasmus

    2005-01-01

    Outliers may hamper proper classical multivariate analysis, and lead to incorrect conclusions. To remedy the problem of outliers, robust methods are developed in statistics and chemometrics. Robust methods reduce or remove the effect of outlying data points and allow the ?good? data to primarily...

  10. Looking back at the gifi system of nonlinear multivariate analysis

    NARCIS (Netherlands)

    van der Heijden, Peter G M; van Buuren, Stef

    2016-01-01

    Gifi was the nom de plume for a group of researchers led by Jan de Leeuw at the University of Leiden. Between 1970 and 1990 the group produced a stream of theoretical papers and computer programs in the area of nonlinear multivariate analysis that were very innovative. In an informal way this paper

  11. TMVA The Toolkit for Multivariate Data Analysis eith ROOT

    CERN Document Server

    Höcker, Andreas; Stelzer, Jörg; Tegenfeldt, Fredrik; Voss, Helge

    2008-01-01

    Multivariate classi cation methods based on machine learning techniques have become a fundamental ingredient to most physics analyses. The classi cation techniques themselves have also signi cantly evolved in recent years. Statisticians have found new ways to tune and to combine classi ers to further gain in performance. Integrated into the analysis framework ROOT, TMVA is a toolkit offering a large variety of multivariate classi cation algorithms. TMVA manages the simultaneous training, testing and performance evaluation of all the classi ers with a user-friendly interface, and also steers the application of the trained classi ers to data.

  12. Recent developments in multivariate pattern analysis for functional MRI

    Institute of Scientific and Technical Information of China (English)

    Zhi Yang; Fang Fang; Xuchu Weng

    2012-01-01

    Multivariate pattern analysis (MVPA) is a recently-developed approach for functional magnetic resonance imaging (fMRI) data analyses.Compared with the traditional univariate methods,MVPA is more sensitive to subtle changes in multivariate patterns in fMRI data.In this review,we introduce several significant advances in MVPA applications and summarize various combinations of algorithms and parameters in different problem settings.The limitations of MVPA and some critical questions that need to be addressed in future research are also discussed.

  13. Multivariate Survival Mixed Models for Genetic Analysis of Longevity Traits

    DEFF Research Database (Denmark)

    Pimentel Maia, Rafael; Madsen, Per; Labouriau, Rodrigo

    2013-01-01

    A class of multivariate mixed survival models for continuous and discrete time with a complex covariance structure is introduced in a context of quantitative genetic applications. The methods introduced can be used in many applications in quantitative genetics although the discussion presented...... concentrates on longevity studies. The framework presented allows to combine models based on continuous time with models based on discrete time in a joint analysis. The continuous time models are approximations of the frailty model in which the hazard function will be assumed to be piece-wise constant....... The discrete time models used are multivariate variants of the discrete relative risk models. These models allow for regular parametric likelihood-based inference by exploring a coincidence of their likelihood functions and the likelihood functions of suitably defined multivariate generalized linear mixed...

  14. Multivariate Survival Mixed Models for Genetic Analysis of Longevity Traits

    DEFF Research Database (Denmark)

    Pimentel Maia, Rafael; Madsen, Per; Labouriau, Rodrigo

    2014-01-01

    A class of multivariate mixed survival models for continuous and discrete time with a complex covariance structure is introduced in a context of quantitative genetic applications. The methods introduced can be used in many applications in quantitative genetics although the discussion presented...... concentrates on longevity studies. The framework presented allows to combine models based on continuous time with models based on discrete time in a joint analysis. The continuous time models are approximations of the frailty model in which the hazard function will be assumed to be piece-wise constant....... The discrete time models used are multivariate variants of the discrete relative risk models. These models allow for regular parametric likelihood-based inference by exploring a coincidence of their likelihood functions and the likelihood functions of suitably defined multivariate generalized linear mixed...

  15. Multivariate image analysis for quality inspection in fish feed production

    DEFF Research Database (Denmark)

    Ljungqvist, Martin Georg

    , or synthesised chemically. Common for both types is that they are relatively expensive in comparison to the other feed ingredients. This thesis investigates multi-variate data collection for visual inspection and optimisation of industrial production in the fish feed industry. Quality parameters focused on here...... are: pellet size, type and concentration level of astaxanthin in pellet coating, as well as astaxanthin type detected in salmonid fish. Methods used are three different devices for multi- and hyper-spectral imaging, together with shape analysis and multi-variate statistical analysis. The results...... of the work demonstrate a high potential of image analysis and spectral imaging for assessing the product quality of fish feed pellets, astaxanthin and fish meat. We show how image analysis can be used to inspect the pellet size, and how spectral imaging can be used to inspect the surface quality...

  16. Multivariate statistical analysis of precipitation chemistry in Northwestern Spain

    Energy Technology Data Exchange (ETDEWEB)

    Prada-Sanchez, J.M.; Garcia-Jurado, I.; Gonzalez-Manteiga, W.; Fiestras-Janeiro, M.G.; Espada-Rios, M.I.; Lucas-Dominguez, T. (University of Santiago, Santiago (Spain). Faculty of Mathematics, Dept. of Statistics and Operations Research)

    1993-07-01

    149 samples of rainwater were collected in the proximity of a power station in northwestern Spain at three rainwater monitoring stations. The resulting data are analyzed using multivariate statistical techniques. Firstly, the Principal Component Analysis shows that there are three main sources of pollution in the area (a marine source, a rural source and an acid source). The impact from pollution from these sources on the immediate environment of the stations is studied using Factorial Discriminant Analysis. 8 refs., 7 figs., 11 tabs.

  17. Kaempferol inhibits fibroblast collagen synthesis, proliferation and activation in hypertrophic scar via targeting TGF-β receptor type I.

    Science.gov (United States)

    Li, Hongwei; Yang, Liu; Zhang, Yuebing; Gao, Zhigang

    2016-10-01

    Hypertrophic scar (HPS) formation is a debilitating condition that results in pain, esthetic symptom and loss of tissue function. So far, no satisfactory therapeutic approach has been available for HPS treatment. In this study, we discovered that a natural small molecule, kaempferol, could significantly inhibit HPS formation in a mechanical load-induced mouse model. Our results also demonstrated that kaempferol remarkably attenuated collagen synthesis, proliferation and activation of fibroblasts in vitro and in vivo. Western blot analysis further revealed that kaempferol significantly down-regulated Smad2 and Smad3 phosphorylation in a dose-dependent manner. At last, we found that such bioactivity of kaempferol which resulted from the inhibition of TGF-β1/Smads signaling was induced by the selective binding of kaempferol to TGF-β receptor type I (TGFβRI). These findings suggest that kaempferol could be developed into a promising agent for the treatment of HPS or other fibroproliferative disorders.

  18. Voxelwise multivariate analysis of multimodality magnetic resonance imaging.

    Science.gov (United States)

    Naylor, Melissa G; Cardenas, Valerie A; Tosun, Duygu; Schuff, Norbert; Weiner, Michael; Schwartzman, Armin

    2014-03-01

    Most brain magnetic resonance imaging (MRI) studies concentrate on a single MRI contrast or modality, frequently structural MRI. By performing an integrated analysis of several modalities, such as structural, perfusion-weighted, and diffusion-weighted MRI, new insights may be attained to better understand the underlying processes of brain diseases. We compare two voxelwise approaches: (1) fitting multiple univariate models, one for each outcome and then adjusting for multiple comparisons among the outcomes and (2) fitting a multivariate model. In both cases, adjustment for multiple comparisons is performed over all voxels jointly to account for the search over the brain. The multivariate model is able to account for the multiple comparisons over outcomes without assuming independence because the covariance structure between modalities is estimated. Simulations show that the multivariate approach is more powerful when the outcomes are correlated and, even when the outcomes are independent, the multivariate approach is just as powerful or more powerful when at least two outcomes are dependent on predictors in the model. However, multiple univariate regressions with Bonferroni correction remain a desirable alternative in some circumstances. To illustrate the power of each approach, we analyze a case control study of Alzheimer's disease, in which data from three MRI modalities are available. Copyright © 2013 Wiley Periodicals, Inc.

  19. PIXE-quantified AXSIA : elemental mapping by multivariate spectral analysis.

    Energy Technology Data Exchange (ETDEWEB)

    Doyle, Barney Lee; Antolak, Arlyn J. (Sandia National Labs, Livermore, CA); Campbell, J. L. (University of Guelph, Guelph, ON, Canada); Ryan, C. G. (CSIRO Exploration and Mining Bayview Road, Clayton VIC, Australia); Provencio, Paula Polyak; Barrett, Keith E. (Primecore Systems, Albuquerque, NM,); Kotula, Paul Gabriel

    2005-07-01

    Automated, nonbiased, multivariate statistical analysis techniques are useful for converting very large amounts of data into a smaller, more manageable number of chemical components (spectra and images) that are needed to describe the measurement. We report the first use of the multivariate spectral analysis program AXSIA (Automated eXpert Spectral Image Analysis) developed at Sandia National Laboratories to quantitatively analyze micro-PIXE data maps. AXSIA implements a multivariate curve resolution technique that reduces the spectral image data sets into a limited number of physically realizable and easily interpretable components (including both spectra and images). We show that the principal component spectra can be further analyzed using conventional PIXE programs to convert the weighting images into quantitative concentration maps. A common elemental data set has been analyzed using three different PIXE analysis codes and the results compared to the cases when each of these codes is used to separately analyze the associated AXSIA principal component spectral data. We find that these comparisons are in good quantitative agreement with each other.

  20. Multivariate meta-analysis using individual participant data.

    Science.gov (United States)

    Riley, R D; Price, M J; Jackson, D; Wardle, M; Gueyffier, F; Wang, J; Staessen, J A; White, I R

    2015-06-01

    When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is that within-study correlations needed to fit the multivariate model are unknown from published reports. However, provision of individual participant data (IPD) allows them to be calculated directly. Here, we illustrate how to use IPD to estimate within-study correlations, using a joint linear regression for multiple continuous outcomes and bootstrapping methods for binary, survival and mixed outcomes. In a meta-analysis of 10 hypertension trials, we then show how these methods enable multivariate meta-analysis to address novel clinical questions about continuous, survival and binary outcomes; treatment-covariate interactions; adjusted risk/prognostic factor effects; longitudinal data; prognostic and multiparameter models; and multiple treatment comparisons. Both frequentist and Bayesian approaches are applied, with example software code provided to derive within-study correlations and to fit the models. © 2014 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd.

  1. A review on the dietary flavonoid kaempferol.

    Science.gov (United States)

    Calderón-Montaño, J M; Burgos-Morón, E; Pérez-Guerrero, C; López-Lázaro, M

    2011-04-01

    Epidemiological studies have revealed that a diet rich in plant-derived foods has a protective effect on human health. Identifying bioactive dietary constituents is an active area of scientific investigation that may lead to new drug discovery. Kaempferol (3,5,7-trihydroxy-2-(4-hydroxyphenyl)-4H-1-benzopyran-4-one) is a flavonoid found in many edible plants (e.g. tea, broccoli, cabbage, kale, beans, endive, leek, tomato, strawberries and grapes) and in plants or botanical products commonly used in traditional medicine (e.g. Ginkgo biloba, Tilia spp, Equisetum spp, Moringa oleifera, Sophora japonica and propolis). Some epidemiological studies have found a positive association between the consumption of foods containing kaempferol and a reduced risk of developing several disorders such as cancer and cardiovascular diseases. Numerous preclinical studies have shown that kaempferol and some glycosides of kaempferol have a wide range of pharmacological activities, including antioxidant, anti-inflammatory, antimicrobial, anticancer, cardioprotective, neuroprotective, antidiabetic, anti-osteoporotic, estrogenic/antiestrogenic, anxiolytic, analgesic and antiallergic activities. In this article, the distribution of kaempferol in the plant kingdom and its pharmacological properties are reviewed. The pharmacokinetics (e.g. oral bioavailability, metabolism, plasma levels) and safety of kaempferol are also analyzed. This information may help understand the health benefits of kaempferol-containing plants and may contribute to develop this flavonoid as a possible agent for the prevention and treatment of some diseases.

  2. Kaempferol slows intervertebral disc degeneration by modifying LPS-induced osteogenesis/adipogenesis imbalance and inflammation response in BMSCs.

    Science.gov (United States)

    Zhu, Jun; Tang, Haoyu; Zhang, Zhenhua; Zhang, Yong; Qiu, Chengfeng; Zhang, Ling; Huang, Pinge; Li, Feng

    2017-02-01

    Intervertebral disc (IVD) degeneration is a common disease that represents a significant cause of socio-economic problems. Bone marrow-derived mesenchymal stem cells (BMSCs) are a potential autologous stem cell source for the nucleus pulposus regeneration. Kaempferol has been reported to exert protective effects against both osteoporosis and obesity. This study explored the effect of kaempferol on BMSCs differentiation and inflammation. The results demonstrated that kaempferol did not show any cytotoxicity at concentrations of 20, 60 and 100μM. Kaempferol enhanced cell viability by counteracting the lipopolysaccharide (LPS)-induced cell apoptosis and increasing cell proliferation. Western blot analysis of mitosis-associated nuclear antigen (Ki67) and proliferation cell nuclear antigen (PCNA) further confirmed the increased effect of kaempferol on LPS-induced decreased viability of BMSCs. Besides, kaempferol elevated LPS-induced reduced level of chondrogenic markers (SOX-9, Collagen II and Aggrecan), decreased the level of matrix-degrading enzymes, i.e., matrix metalloprotease (MMP)-3 and MMP-13, suggesting the osteogenesis of BMSC under kaempferol treatment. On the other hand, kaempferol enhanced LPS-induced decreased expression of lipid catabolism-related genes, i.e., carnitine palmitoyl transferase-1 (CPT-1). Kaempferol also suppressed the expression of lipid anabolism-related genes, i.e., peroxisome proliferators-activated receptor-γ (PPAR-γ). The Oil red O staining further convinced the inhibition effect of kaempferol on BMSCs adipogenesis. In addition, kaempferol alleviated inflammatory by reducing the level of pro-inflammatory cytokines (i.e., interleukin (IL)-6) and increasing anti-inflammatory cytokine (IL-10) via inhibiting the nucleus translocation of nuclear transcription factor (NF)-κB p65. Taken together, our research indicated that kaempferol may serve as a novel target for treatment of IVD degeneration.

  3. Multivariate statistical analysis a high-dimensional approach

    CERN Document Server

    Serdobolskii, V

    2000-01-01

    In the last few decades the accumulation of large amounts of in­ formation in numerous applications. has stimtllated an increased in­ terest in multivariate analysis. Computer technologies allow one to use multi-dimensional and multi-parametric models successfully. At the same time, an interest arose in statistical analysis with a de­ ficiency of sample data. Nevertheless, it is difficult to describe the recent state of affairs in applied multivariate methods as satisfactory. Unimprovable (dominating) statistical procedures are still unknown except for a few specific cases. The simplest problem of estimat­ ing the mean vector with minimum quadratic risk is unsolved, even for normal distributions. Commonly used standard linear multivari­ ate procedures based on the inversion of sample covariance matrices can lead to unstable results or provide no solution in dependence of data. Programs included in standard statistical packages cannot process 'multi-collinear data' and there are no theoretical recommen­ ...

  4. Symbolic observability coefficients for univariate and multivariate analysis

    Science.gov (United States)

    Letellier, Christophe; Aguirre, Luis A.

    2009-06-01

    In practical problems, the observability of a system not only depends on the choice of observable(s) but also on the space which is reconstructed. In fact starting from a given set of observables, the reconstructed space is not unique, since the dimension can be varied and, in the case of multivariate measurement functions, there are various ways to combine the measured observables. Using a graphical approach recently introduced, we analytically compute symbolic observability coefficients which allow to choose from the system equations the best observable, in the case of scalar reconstructions, and the best way to combine the observables in the case of multivariate reconstructions. It is shown how the proposed coefficients are also helpful for analysis in higher dimension.

  5. Multiscale Analysis of Information Dynamics for Linear Multivariate Processes

    CERN Document Server

    Faes, Luca; Stramaglia, Sebastiano; Nollo, Giandomenico; Stramaglia, Sebastiano

    2016-01-01

    In the study of complex physical and physiological systems represented by multivariate time series, an issue of great interest is the description of the system dynamics over a range of different temporal scales. While information-theoretic approaches to the multiscale analysis of complex dynamics are being increasingly used, the theoretical properties of the applied measures are poorly understood. This study introduces for the first time a framework for the analytical computation of information dynamics for linear multivariate stochastic processes explored at different time scales. After showing that the multiscale processing of a vector autoregressive (VAR) process introduces a moving average (MA) component, we describe how to represent the resulting VARMA process using state-space (SS) models and how to exploit the SS model parameters to compute analytical measures of information storage and information transfer for the original and rescaled processes. The framework is then used to quantify multiscale infor...

  6. Dating violence, social learning theory, and gender: a multivariate analysis.

    Science.gov (United States)

    Tontodonato, P; Crew, B K

    1992-01-01

    The study of violence between dating partners is a logical extension of interest in marital violence. However, little of this research tests explanations of intimate violence using multivariate techniques, and only recently have such tests occurred within a theoretical framework. Drawing on a recent social learning model of courtship violence (Riggs & O'Leary, 1989), this paper empirically examines constructs hypothesized to be predictive of the use of dating violence and investigates possible gender differences in the underlying causal structure of such violence. Logit analysis indicates that parent-child violence, drug use, and knowledge of use of dating violence by others predict the use of courtship violence by females. Belief that violence between intimates is justifiable, drug use, and parental divorce are related to perpetration of dating aggression by males. Explanations for these results and the importance of a multivariate approach to the problem are discussed.

  7. Simplifying multivariate survival analysis using global score test methodology

    Science.gov (United States)

    Zain, Zakiyah; Aziz, Nazrina; Ahmad, Yuhaniz

    2015-12-01

    In clinical trials, the main purpose is often to compare efficacy between experimental and control treatments. Treatment comparisons often involve multiple endpoints, and this situation further complicates the analysis of survival data. In the case of tumor patients, endpoints concerning survival times include: times from tumor removal until the first, the second and the third tumor recurrences, and time to death. For each patient, these endpoints are correlated, and the estimation of the correlation between two score statistics is fundamental in derivation of overall treatment advantage. In this paper, the bivariate survival analysis method using the global score test methodology is extended to multivariate setting.

  8. Handbook of univariate and multivariate data analysis with IBM SPSS

    CERN Document Server

    Ho, Robert

    2013-01-01

    Using the same accessible, hands-on approach as its best-selling predecessor, the Handbook of Univariate and Multivariate Data Analysis with IBM SPSS, Second Edition explains how to apply statistical tests to experimental findings, identify the assumptions underlying the tests, and interpret the findings. This second edition now covers more topics and has been updated with the SPSS statistical package for Windows.New to the Second EditionThree new chapters on multiple discriminant analysis, logistic regression, and canonical correlationNew section on how to deal with missing dataCoverage of te

  9. Covariate selection in multivariate spatial analysis of ovine parasitic infection.

    Science.gov (United States)

    Musella, V; Catelan, D; Rinaldi, L; Lagazio, C; Cringoli, G; Biggeri, A

    2011-05-01

    Gastrointestinal (GI) strongyle and fluke infections remain one of the main constraints on health and productivity in sheep dairy production. A cross-sectional survey was conducted in 2004-2005 on ovine farms in the Campania region of southern Italy in order to evaluate the prevalence of Haemonchus contortus, Fasciola hepatica, Dicrocoelium dendriticum and Calicophoron daubneyi from among other parasitic infections. In the present work, we focused on the role of the ecological characteristics of the pasture environment while accounting for the underlying long range geographical risk pattern. Bayesian multivariate spatial statistical analysis was used. A systematic grid (10 km×10 km) sampling approach was used. Laboratory procedures were based on the FLOTAC technique to detect and count eggs of helminths. A Geographical Information System (GIS) was constructed by using environmental data layers. Data on each of these layers were then extracted for pasturing areas that were previously digitalized aerial images of the ovine farms. Bayesian multivariate statistical analyses, including improper multivariate conditional autoregressive models, were used to select covariates on a multivariate spatially structured risk surface. Out of the 121 tested farms, 109 were positive for H. contortus, 81 for D. dendriticum, 17 for C. daubneyi and 15 for F. hepatica. The statistical analysis highlighted a north-south long range spatially structured pattern. This geographical pattern is treated here as a confounder, because the main interest was in the causal role of ecological covariates at the level of each pasturing area. A high percentage of pasture and impermeable soil were strong predictors of F. hepatica risk and a high percentage of wood was a strong predictor of C. daubneyi. A high percentage of wood, rocks and arable soil with sparse trees explained the spatial distribution of D. dendriticum. Sparse vegetation, river, mixed soil and permeable soil explained the spatial

  10. Multivariate Analysis and Prediction of Dioxin-Furan ...

    Science.gov (United States)

    Peer Review Draft of Regional Methods Initiative Final Report Dioxins, which are bioaccumulative and environmentally persistent, pose an ongoing risk to human and ecosystem health. Fish constitute a significant source of dioxin exposure for humans and fish-eating wildlife. Current dioxin analytical methods are costly, time-consuming, and produce hazardous by-products. A Danish team developed a novel, multivariate statistical methodology based on the covariance of dioxin-furan congener Toxic Equivalences (TEQs) and fatty acid methyl esters (FAMEs) and applied it to North Atlantic Ocean fishmeal samples. The goal of the current study was to attempt to extend this Danish methodology to 77 whole and composite fish samples from three trophic groups: predator (whole largemouth bass), benthic (whole flathead and channel catfish) and forage fish (composite bluegill, pumpkinseed and green sunfish) from two dioxin contaminated rivers (Pocatalico R. and Kanawha R.) in West Virginia, USA. Multivariate statistical analyses, including, Principal Components Analysis (PCA), Hierarchical Clustering, and Partial Least Squares Regression (PLS), were used to assess the relationship between the FAMEs and TEQs in these dioxin contaminated freshwater fish from the Kanawha and Pocatalico Rivers. These three multivariate statistical methods all confirm that the pattern of Fatty Acid Methyl Esters (FAMEs) in these freshwater fish covaries with and is predictive of the WHO TE

  11. Selective methylation of kaempferol via benzylation and deacetylation of kaempferol acetates

    OpenAIRE

    Qinggang Mei; Chun Wang; Weicheng Yuan; Guolin Zhang

    2015-01-01

    A strategy for selective mono-, di- and tri-O-methylation of kaempferol, predominantly on the basis of selective benzylation and controllable deacetylation of kaempferol acetates, was developed. From the selective deacetylation and benzylation of kaempferol tetraacetate (1), 3,4′,5,-tri-O-acetylkaempferol (2) and 7-O-benzyl-3,4′5,-tri-O-acetylkaempferol (8) were obtained, respectively. By controllable deacetylation and followed selective or direct methylation of these two intermediates, eight...

  12. The mechanism of kaempferol induced apoptosis and inhibited proliferation in human cervical cancer SiHa cell: From macro to nano.

    Science.gov (United States)

    Tu, Lv-Ying; Bai, Hai-Hua; Cai, Ji-Ye; Deng, Sui-Ping

    2016-11-01

    Kaempferol has been identified as a potential cancer therapeutic agent by an increasing amount of evidences. However, the changes in the topography of cell membrane induced by kaempferol at subcellular- or nanometer-level were still unclear. In this work, the topographical changes of cytomembrane in human cervical cancer cell (SiHa) induced by kaempferol, as well as the role of kaempferol in apoptosis induction and its possible mechanisms, were investigated. At the macro level, MTT assays showed that kaempferol inhibited the proliferation of SiHa cells in a time- and dose-dependent manner. Flow cytometry analysis demonstrated that kaempferol could induce SiHa cell apoptosis, mitochondrial membrane potential disruption, and intracellular free calcium elevation. At the micro level, fluorescence imaging by laser scanning confocal microscopy (LSCM) indicated that kaempferol could also destroy the networks of microtubules. Using high resolution atomic force microscopy (AFM), we determined the precise changes of cellular membrane induced by kaempferol at subcellular or nanometer level. The spindle-shaped SiHa cells shrank after kaempferol treatment, with significantly increased cell surface roughness. These data showed structural characterizations of cellular topography in kaempferol-induced SiHa cell apoptosis and might provide novel integrated information from macro to nano level to assess the impact of kaempferol on cancer cells, which might be important for the understanding of the anti-cancer mechanisms of drugs. SCANNING 38:644-653, 2016. © 2016 Wiley Periodicals, Inc.

  13. Multivariate time series analysis with R and financial applications

    CERN Document Server

    Tsay, Ruey S

    2013-01-01

    Since the publication of his first book, Analysis of Financial Time Series, Ruey Tsay has become one of the most influential and prominent experts on the topic of time series. Different from the traditional and oftentimes complex approach to multivariate (MV) time series, this sequel book emphasizes structural specification, which results in simplified parsimonious VARMA modeling and, hence, eases comprehension. Through a fundamental balance between theory and applications, the book supplies readers with an accessible approach to financial econometric models and their applications to real-worl

  14. [Neuroimaging in psychiatry: multivariate analysis techniques for diagnosis and prognosis].

    Science.gov (United States)

    Kambeitz, J; Koutsouleris, N

    2014-06-01

    Multiple studies successfully applied multivariate analysis to neuroimaging data demonstrating the potential utility of neuroimaging for clinical diagnostic and prognostic purposes. Summary of the current state of research regarding the application of neuroimaging in the field of psychiatry. Literature review of current studies. Results of current studies indicate the potential application of neuroimaging data across various diagnoses, such as depression, schizophrenia, bipolar disorder and dementia. Potential applications include disease classification, differential diagnosis and prediction of disease course. The results of the studies are heterogeneous although some studies report promising findings. Further multicentre studies are needed with clearly specified patient populations to systematically investigate the potential utility of neuroimaging for the clinical routine.

  15. Multivariate Analysis of Clinical Factors in Restenosis after Coronary Stenting

    Institute of Scientific and Technical Information of China (English)

    Wen Shangyu; Mao Jieming; Guo Liiun; Zhao Yiming; Zhang Fuchun; Guo Jingxlan; Cheng Mingzhe

    2000-01-01

    Ojbective To find the independent predictors for restenosis after coronary stenting.Methods Quantitative angiography was performed on 60 cases (67 successfully dilated lesions) after angioplasty over 6-months follow-up, and both univariate and multivariate logistic regression analysis were done to identify the correlations of restenosis with clinical factors. Results The total restenosis rate was 31.3%(21 of 67 lesions), and according to univariate analysis the patients who underwent coronary stenting ≥3.5mm had a lower rate of restenosis ( P < 0. 01).Collateral circulation to the obstruction site, high maximal inflation pressure, smoking and the less minimal lumen diameter after PTCA made the rate of restenosis higherower ( P < 0.05) . Multivariate logistic regression analysis showed that coronary stenting ≥ 3.5mm had a low rate of restenosis, but high maximal inflation pressure and smoking made the restenosis rate higher. Conclusion Coronary stent size, maximal inflation pressure and. smoking were independent predictors for restenosis.

  16. A Comparative Analysis of Multivariate Statistical Detection Methods Applied to Syndromic Surveillance

    Science.gov (United States)

    2007-06-01

    the observed system. Our research involved a comparative analysis of two multivariate statistical methods, the multivariate CUSUM (MCUSUM) and the...outbreaks. We found that, similar to results for the univariate CUSUM and EWMA, the directionally-sensitive MCUSUM and MEWMA perform very similarly. 14...SUBJECT TERMS Biosurveillance, Multivariate CUSUM , Multivariate EWMA, Statistical Process Control, Syndromic Surveillance 15. NUMBER OF PAGES

  17. Multivariate analysis in dam monitoring data with PCA

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    Given the limitation of traditional univariate analysis method in processing the multicollinearity of dam monitoring data,this paper reconstructs the multivariate response variables by introducing principal component analysis(PCA) method,explores the ways of determining principal components(PCs),and extracts a few PCs that have major influence on data variance.For steady observation series,a control field for the whole observation values has been established based upon PCA;for unsteady observation series that have significant tendency,a control field for the future observation values has been constructed according to PC statistical predication model.These methods have already been applied to an actual project and the results showed that data interpretation method with PCA can not only realize data reduction,lower data redundancy,and reduce noise and false alarm rate,but also be effective to data analysis,having a broad application prospect.

  18. Multivariate analysis of the hunting tactics of Kalahari leopards

    Directory of Open Access Journals (Sweden)

    J. du P. Bothma

    1997-01-01

    Full Text Available The hunting tactics of male and female leopards in the southern Kalahari were analysed for prey-specific patterns. The field study was based on tracking leopard spoor in the sandy substrate of the Kalahari. Visual profiles for each type of prey were compiled for various facets of hunting. Data sets were analysed further, using Correspondence Analysis and Detrended Correspondence Analysis. The results indicate that multivariate analysis can be used to demonstrate prey-specific hunting tactics in Kalahari leopards. In using a scarce prey base, Kalahari leopards seem to be number maximisers as they are unselective of prey type, age or sex. The presence of prey-specific hunting tactics may indicate a move along a continuum towards some degree of energy maximisation.

  19. Forensic discrimination of dyed hair color: II. Multivariate statistical analysis.

    Science.gov (United States)

    Barrett, Julie A; Siegel, Jay A; Goodpaster, John V

    2011-01-01

    This research is intended to assess the ability of UV-visible microspectrophotometry to successfully discriminate the color of dyed hair. Fifty-five red hair dyes were analyzed and evaluated using multivariate statistical techniques including agglomerative hierarchical clustering (AHC), principal component analysis (PCA), and discriminant analysis (DA). The spectra were grouped into three classes, which were visually consistent with different shades of red. A two-dimensional PCA observations plot was constructed, describing 78.6% of the overall variance. The wavelength regions associated with the absorbance of hair and dye were highly correlated. Principal components were selected to represent 95% of the overall variance for analysis with DA. A classification accuracy of 89% was observed for the comprehensive dye set, while external validation using 20 of the dyes resulted in a prediction accuracy of 75%. Significant color loss from successive washing of hair samples was estimated to occur within 3 weeks of dye application.

  20. Multivariate Statistical Analysis Applied in Wine Quality Evaluation

    Directory of Open Access Journals (Sweden)

    Jieling Zou

    2015-08-01

    Full Text Available This study applies multivariate statistical approaches to wine quality evaluation. With 27 red wine samples, four factors were identified out of 12 parameters by principal component analysis, explaining 89.06% of the total variance of data. As iterative weights calculated by the BP neural network revealed little difference from weights determined by information entropy method, the latter was chosen to measure the importance of indicators. Weighted cluster analysis performs well in classifying the sample group further into two sub-clusters. The second cluster of red wine samples, compared with its first, was lighter in color, tasted thinner and had fainter bouquet. Weighted TOPSIS method was used to evaluate the quality of wine in each sub-cluster. With scores obtained, each sub-cluster was divided into three grades. On the whole, the quality of lighter red wine was slightly better than the darker category. This study shows the necessity and usefulness of multivariate statistical techniques in both wine quality evaluation and parameter selection.

  1. Micro-Raman Imaging for Biology with Multivariate Spectral Analysis

    KAUST Repository

    Malvaso, Federica

    2015-05-05

    Raman spectroscopy is a noninvasive technique that can provide complex information on the vibrational state of the molecules. It defines the unique fingerprint that allow the identification of the various chemical components within a given sample. The aim of the following thesis work is to analyze Raman maps related to three pairs of different cells, highlighting differences and similarities through multivariate algorithms. The first pair of analyzed cells are human embryonic stem cells (hESCs), while the other two pairs are induced pluripotent stem cells (iPSCs) derived from T lymphocytes and keratinocytes, respectively. Although two different multivariate techniques were employed, ie Principal Component Analysis and Cluster Analysis, the same results were achieved: the iPSCs derived from T-lymphocytes show a higher content of genetic material both compared with the iPSCs derived from keratinocytes and the hESCs . On the other side, equally evident, was that iPS cells derived from keratinocytes assume a molecular distribution very similar to hESCs.

  2. Multivariate analysis of craniometric characters in Bulgarian chamois

    Directory of Open Access Journals (Sweden)

    Giovanna Massei

    1994-05-01

    Full Text Available Abstract A craniometrical study was carried out to examine the skull characteristics of the Bulgarian chamois (Rupicapra rupicapra balcanica (1 to assess whether any difference between sexes is detectable and (2 to compare the Bulgarian material with other already described chamois populations occurring in other European regions. Results of multivariate analyses run on seven craniometrical characters showed sexual dimorphism in the Bulgarian sample. Discriminant Analysis performed on individuals from different populations showed that the positions of the samples in discriminant space were approximately congruent with their geographical position. Principal Component Analysis revealed that the main factor of variation among groups is a size factor. The structure of loadings on PC-II and PC-III and the amount of total variability expressed by these two components suggested also shape differences. Results from multivariate analyses carried out on the means of the characters confirmed these patterns. A dimensional cline for the genus Rupicapra is suggested, the north-east chamois populations showing the largest skulls and the south-west populations having the smallest sizes. Riassunto Analisi multivariata dei caratteri craniometrici ne1 camoscio bulgaro - Uno studio dei caratteri cranici del camoscio bulgaro (Rupicapra rupicapra balcanica è stato effettuato a1 fine di 1 valutare il grado di dimorfismo sessuale; 2 confrontare il campione bulgaro con altre popolazioni di camoscio europeo già descritte in letteratura. I risultati delle analisi multivariate effettuate su sette caratteri craniometrici hanno mostrato l'esistenza del dimorfismo sessuale nel camoscio bulgaro. L'analisi discriminante effettuata su individui appartenenti a diverse popolazioni ha mostrato che la posizione dei campioni nello spazio discriminante è congruente con la loro posizione geografica. L

  3. Multivariate analysis of quantitative traits can effectively classify rapeseed germplasm

    Directory of Open Access Journals (Sweden)

    Jankulovska Mirjana

    2014-01-01

    Full Text Available In this study, the use of different multivariate approaches to classify rapeseed genotypes based on quantitative traits has been presented. Tree regression analysis, PCA analysis and two-way cluster analysis were applied in order todescribe and understand the extent of genetic variability in spring rapeseed genotype by trait data. The traits which highly influenced seed and oil yield in rapeseed were successfully identified by the tree regression analysis. Principal predictor for both response variables was number of pods per plant (NP. NP and 1000 seed weight could help in the selection of high yielding genotypes. High values for both traits and oil content could lead to high oil yielding genotypes. These traits may serve as indirect selection criteria and can lead to improvement of seed and oil yield in rapeseed. Quantitative traits that explained most of the variability in the studied germplasm were classified using principal component analysis. In this data set, five PCs were identified, out of which the first three PCs explained 63% of the total variance. It helped in facilitating the choice of variables based on which the genotypes’ clustering could be performed. The two-way cluster analysissimultaneously clustered genotypes and quantitative traits. The final number of clusters was determined using bootstrapping technique. This approach provided clear overview on the variability of the analyzed genotypes. The genotypes that have similar performance regarding the traits included in this study can be easily detected on the heatmap. Genotypes grouped in the clusters 1 and 8 had high values for seed and oil yield, and relatively short vegetative growth duration period and those in cluster 9, combined moderate to low values for vegetative growth duration and moderate to high seed and oil yield. These genotypes should be further exploited and implemented in the rapeseed breeding program. The combined application of these multivariate methods

  4. Contributions to multivariate analysis with applications in marketing

    NARCIS (Netherlands)

    Perlo-ten Kleij, Frederieke van

    2004-01-01

    Dit proefschrift behandelt een aantal onderwerpen uit de multivariate analyse, waarbij het begrip ‘multivariate analyse’ ruim moet worden ge¨ınterpreteerd. Naast onderwerpen uit de multivariate statistiek in enge zin, besteden we ook aandacht aan matrixrekening, ‘sum-constrained linear models’, mark

  5. Contributions to multivariate analysis with applications in marketing

    NARCIS (Netherlands)

    Perlo-ten Kleij, Frederieke van

    2004-01-01

    Dit proefschrift behandelt een aantal onderwerpen uit de multivariate analyse, waarbij het begrip ‘multivariate analyse’ ruim moet worden ge¨ınterpreteerd. Naast onderwerpen uit de multivariate statistiek in enge zin, besteden we ook aandacht aan matrixrekening, ‘sum-constrained linear models’,

  6. Environmental Performance in Countries Worldwide: Determinant Factors and Multivariate Analysis

    Directory of Open Access Journals (Sweden)

    Isabel Gallego-Alvarez

    2014-11-01

    Full Text Available The aim of this study is to analyze the environmental performance of countries and the variables that can influence it. At the same time, we performed a multivariate analysis using the HJ-biplot, an exploratory method that looks for hidden patterns in the data, obtained from the usual singular value decomposition (SVD of the data matrix, to contextualize the countries grouped by geographical areas and the variables relating to environmental indicators included in the environmental performance index. The sample used comprises 149 countries of different geographic areas. The findings obtained from the empirical analysis emphasize that socioeconomic factors, such as economic wealth and education, as well as institutional factors represented by the style of public administration, in particular control of corruption, are determinant factors of environmental performance in the countries analyzed. In contrast, no effect on environmental performance was found for factors relating to the internal characteristics of a country or political factors.

  7. Multivariate Analysis of Blood Transfusion Rates After Shoulder Arthroplasty.

    Science.gov (United States)

    King, Joseph J; Patrick, Matthew R; Schnetzer, Ryan E; Farmer, Kevin W; Struk, Aimee M; Garvan, Cyndi; Wright, Thomas W

    A retrospective review was performed of all shoulder arthroplasties with patients grouped on the basis of transfusion protocol time period. Group 1 had transfusions if postoperative hematocrit was multivariate analysis of significant bivariate factors were performed. Protocol change decreased transfusion rates from 16% (group 1, 153 arthroplasties) to 8% (group 2, 149 arthroplasties). Reverse shoulder arthroplasty (RTSA) transfusion rate decreased dramatically (from 24% to 5%). Transfusion rates after total shoulder arthroplasty (TSA) were low (4%) and after revision arthroplasty were high (21% + 27%) in both groups. Age, gender, heart disease, preoperative hematocrit, diagnosis, and estimated blood loss (EBL) were risk factors on bivariate analysis. Failed arthroplasty and fracture diagnoses carried high transfusion rates (25% + 28%). Logistic regression showed that low preoperative hematocrit, increased EBL, revision arthroplasty, and heart disease were transfusion risk factors. Protocol based on symptomatic anemia results in low transfusion rates after primary TSA and RTSA.

  8. Processes and subdivisions in diogenites, a multivariate statistical analysis

    Science.gov (United States)

    Harriott, T. A.; Hewins, R. H.

    1984-01-01

    Multivariate statistical techniques used on diogenite orthopyroxene analyses show the relationships that occur within diogenites and the two orthopyroxenite components (class I and II) in the polymict diogenite Garland. Cluster analysis shows that only Peckelsheim is similar to Garland class I (Fe-rich) and the other diogenites resemble Garland class II. The unique diogenite Y 75032 may be related to type I by fractionation. Factor analysis confirms the subdivision and shows that Fe does not correlate with the weakly incompatible elements across the entire pyroxene composition range, indicating that igneous fractionation is not the process controlling total diogenite composition variation. The occurrence of two groups of diogenites is interpreted as the result of sampling or mixing of two main sequences of orthopyroxene cumulates with slightly different compositions.

  9. Multivariate space - time analysis of PRE-STORM precipitation

    Science.gov (United States)

    Polyak, Ilya; North, Gerald R.; Valdes, Juan B.

    1994-01-01

    This paper presents the methodologies and results of the multivariate modeling and two-dimensional spectral and correlation analysis of PRE-STORM rainfall gauge data. Estimated parameters of the models for the specific spatial averages clearly indicate the eastward and southeastward wave propagation of rainfall fluctuations. A relationship between the coefficients of the diffusion equation and the parameters of the stochastic model of rainfall fluctuations is derived that leads directly to the exclusive use of rainfall data to estimate advection speed (about 12 m/s) as well as other coefficients of the diffusion equation of the corresponding fields. The statistical methodology developed here can be used for confirmation of physical models by comparison of the corresponding second-moment statistics of the observed and simulated data, for generating multiple samples of any size, for solving the inverse problem of the hydrodynamic equations, and for application in some other areas of meteorological and climatological data analysis and modeling.

  10. Multivariate data analysis of enzyme production for hydrolysis purposes

    DEFF Research Database (Denmark)

    Schmidt, A.S.; Suhr, K.I.

    1999-01-01

    of the structure in the data - possibly combined with analysis of variance (ANOVA). Partial least squares regression (PLSR) showed a clear connection between the two differentdata matrices (the fermentation variables and the hydrolysis variables). Hence, PLSR was suitable for prediction purposes. The hydrolysis......Data from enzyme production experiments were analysed using different multivariate methods. The data set comprised of 12 objects (3 fungi (¤Aspergillus oryzae, Aspergillus fumigatur, Trichoderma reesei¤) grown on 4 substrates (lenzing and/or wet-oxidisedzylan)) and 12 variables (pH, biomass, 7...... enzyme activities (xylanase, zylosidase, arabinosidase, cellulase, acetyl zylan esterase, glucuronidase, feroyl esterase) and 3 hydrolysis efficiencies (reducing suggars at 3 different enzyme loadings)). Principalcomponent analysis (PCA) proved to be an efficient method to obtain an overview...

  11. A multivariate analysis of Antarctic sea ice since 1979

    Energy Technology Data Exchange (ETDEWEB)

    Magalhaes Neto, Newton de; Evangelista, Heitor [Universidade do Estado do Rio de Janeiro (Uerj), LARAMG - Laboratorio de Radioecologia e Mudancas Globais, Maracana, Rio de Janeiro, RJ (Brazil); Tanizaki-Fonseca, Kenny [Universidade do Estado do Rio de Janeiro (Uerj), LARAMG - Laboratorio de Radioecologia e Mudancas Globais, Maracana, Rio de Janeiro, RJ (Brazil); Universidade Federal Fluminense (UFF), Dept. Analise Geoambiental, Inst. de Geociencias, Niteroi, RJ (Brazil); Penello Meirelles, Margareth Simoes [Universidade do Estado do Rio de Janeiro (UERJ)/Geomatica, Maracana, Rio de Janeiro, RJ (Brazil); Garcia, Carlos Eiras [Universidade Federal do Rio Grande (FURG), Laboratorio de Oceanografia Fisica, Rio Grande, RS (Brazil)

    2012-03-15

    Recent satellite observations have shown an increase in the total extent of Antarctic sea ice, during periods when the atmosphere and oceans tend to be warmer surrounding a significant part of the continent. Despite an increase in total sea ice, regional analyses depict negative trends in the Bellingshausen-Amundsen Sea and positive trends in the Ross Sea. Although several climate parameters are believed to drive the formation of Antarctic sea ice and the local atmosphere, a descriptive mechanism that could trigger such differences in trends are still unknown. In this study we employed a multivariate analysis in order to identify the response of the Antarctic sea ice with respect to commonly utilized climate forcings/parameters, as follows: (1) The global air surface temperature, (2) The global sea surface temperature, (3) The atmospheric CO{sub 2} concentration, (4) The South Annular Mode, (5) The Nino 3, (6) The Nino (3 + 4, 7) The Nino 4, (8) The Southern Oscillation Index, (9) The Multivariate ENSO Index, (10) the Total Solar Irradiance, (11) The maximum O{sub 3} depletion area, and (12) The minimum O{sub 3} concentration over Antarctica. Our results indicate that western Antarctic sea ice is simultaneously impacted by several parameters; and that the minimum, mean, and maximum sea ice extent may respond to a separate set of climatic/geochemical parameters. (orig.)

  12. Multivariate Nonlinear Analysis and Prediction of Shanghai Stock Market

    Directory of Open Access Journals (Sweden)

    Junhai Ma

    2008-01-01

    Full Text Available This study attempts to characterize and predict stock returns series in Shanghai stock exchange using the concepts of nonlinear dynamical theory. Surrogate data method of multivariate time series shows that all the stock returns time series exhibit nonlinearity. Multivariate nonlinear prediction methods and univariate nonlinear prediction method, all of which use the concept of phase space reconstruction, are considered. The results indicate that multivariate nonlinear prediction model outperforms univariate nonlinear prediction model, local linear prediction method of multivariate time series outperforms local polynomial prediction method, and BP neural network method. Multivariate nonlinear prediction model is a useful tool for stock price prediction in emerging markets.

  13. Kaempferol and Kaempferol Rhamnosides with Depigmenting and Anti-Inflammatory Properties

    Directory of Open Access Journals (Sweden)

    Jae Youl Cho

    2011-04-01

    Full Text Available The objective of this study was to examine the biological activity of kaempferol and its rhamnosides. We isolated kaempferol (1, a-rhamnoisorobin (2, afzelin (3, and kaempferitrin (4 as pure compounds by far-infrared (FIR irradiation of kenaf (Hibiscus cannabinus L. leaves. The depigmenting and anti-inflammatory activity of the compounds was evaluated by analyzing their structure-activity relationships. The order of the inhibitory activity with regard to depigmentation and nitric oxide (NO production was kaempferol (1 > a-rhamnoisorobin (2 > afzelin (3 > kaempferitrin (4. However, a-rhamnoisorobin (2 was more potent than kaempferol (1 in NF-kB-mediated luciferase assays. From these results, we conclude that the 3-hydroxyl group of kaempferol is an important pharmacophore and that additional rhamnose moieties affect the biological activity negatively.

  14. Profiling and classification of French propolis by combined multivariate data analysis of planar chromatograms and scanning direct analysis in real time mass spectra.

    Science.gov (United States)

    Chasset, Thibaut; Häbe, Tim T; Ristivojevic, Petar; Morlock, Gertrud E

    2016-09-23

    Quality control of propolis is challenging, as it is a complex natural mixture of compounds, and thus, very difficult to analyze and standardize. Shown on the example of 30 French propolis samples, a strategy for an improved quality control was demonstrated in which high-performance thin-layer chromatography (HPTLC) fingerprints were evaluated in combination with selected mass signals obtained by desorption-based scanning mass spectrometry (MS). The French propolis sample extracts were separated by a newly developed reversed phase (RP)-HPTLC method. The fingerprints obtained by two different detection modes, i.e. after (1) derivatization and fluorescence detection (FLD) at UV 366nm and (2) scanning direct analysis in real time (DART)-MS, were analyzed by multivariate data analysis. Thus, RP-HPTLC-FLD and RP-HPTLC-DART-MS fingerprints were explored and the best classification was obtained using both methods in combination with pattern recognition techniques, such as principal component analysis. All investigated French propolis samples were divided in two types and characteristic patterns were observed. Phenolic compounds such as caffeic acid, p-coumaric acid, chrysin, pinobanksin, pinobanksin-3-acetate, galangin, kaempferol, tectochrysin and pinocembrin were identified as characteristic marker compounds of French propolis samples. This study expanded the research on the European poplar type of propolis and confirmed the presence of two botanically different types of propolis, known as the blue and orange types. Copyright © 2016 Elsevier B.V. All rights reserved.

  15. Sedimentary chemofacies characterization by means of multivariate analysis

    Science.gov (United States)

    Montero-Serrano, Jean Carlos; Palarea-Albaladejo, Javier; Martín-Fernández, Josep A.; Martínez-Santana, Manuel; Gutiérrez-Martín, José Vicente

    2010-07-01

    Multivariate statistical analysis is applied to geochemical data from three sections forming part of the stratigraphic record of the Cerro Pelado Formation (Oligocene-Miocene), in the central region of the Falcón Basin, northwestern Venezuela. Our main goal is introducing and testing a statistical protocol for the identification of chemofacies in the studied sections. The first step involves data preparation and cleaning: selection of relevant components, convenient replacement of values below the detection limit and determination of outliers. Second, a biplot analysis allows us to infer geochemical processes that can be interpreted from a paleoenvironmental point of view: detrital association, redox-organic matter association and carbonatic association. Considering such geochemical associations, a constrained cluster analysis is then carried out to determine the chemofacies for each section. According to the compositional nature of geochemical data, all statistical analysis is conducted within a log-ratio analysis framework. In addition, robust statistical methods are considered for outlier detection and biplot representation in order to smooth the influence of potential outliers on the estimates.

  16. Multivariate Regression Analysis of Gravitational Waves from Rotating Core Collapse

    CERN Document Server

    Engels, William J; Ott, Christian D

    2014-01-01

    We present a new multivariate regression model for analysis and parameter estimation of gravitational waves observed from well but not perfectly modeled sources such as core-collapse supernovae. Our approach is based on a principal component decomposition of simulated waveform catalogs. Instead of reconstructing waveforms by direct linear combination of physically meaningless principal components, we solve via least squares for the relationship that encodes the connection between chosen physical parameters and the principal component basis. Although our approach is linear, the waveforms' parameter dependence may be non-linear. For the case of gravitational waves from rotating core collapse, we show, using statistical hypothesis testing, that our method is capable of identifying the most important physical parameters that govern waveform morphology in the presence of simulated detector noise. We also demonstrate our method's ability to predict waveforms from a principal component basis given a set of physical ...

  17. Ordinary chondrites - Multivariate statistical analysis of trace element contents

    Science.gov (United States)

    Lipschutz, Michael E.; Samuels, Stephen M.

    1991-01-01

    The contents of mobile trace elements (Co, Au, Sb, Ga, Se, Rb, Cs, Te, Bi, Ag, In, Tl, Zn, and Cd) in Antarctic and non-Antarctic populations of H4-6 and L4-6 chondrites, were compared using standard multivariate discriminant functions borrowed from linear discriminant analysis and logistic regression. A nonstandard randomization-simulation method was developed, making it possible to carry out probability assignments on a distribution-free basis. Compositional differences were found both between the Antarctic and non-Antarctic H4-6 chondrite populations and between two L4-6 chondrite populations. It is shown that, for various types of meteorites (in particular, for the H4-6 chondrites), the Antarctic/non-Antarctic compositional difference is due to preterrestrial differences in the genesis of their parent materials.

  18. Multivariate Granger Causality Analysis of Obesity Related Variables

    Science.gov (United States)

    Mukhopadhyay, Nitai D; Wheeler, David; Sabo, Roy; Sun, Shumei S

    2015-01-01

    Obesity is a complex health outcome that is a combination of multiple health indicators. Here we attempt to explore the dependence network among multiple aspects of obesity. Two longitudinal cohort studies across multiple decades have been used. The concept of causality is defined similar to Granger causality among multiple time series, however, modified to accommodate multivariate time series as the nodes of the network. Our analysis reveals relatively central position of physical measurements and blood chemistry measures in the overall network across both genders. Also there are some patterns specific to only male or female population. The geometry of the causality network is expected to help in our strategy to control the increasing trend of obesity rate. PMID:26855968

  19. Jelly pineapple syneresis assessment via univariate and multivariate analysis

    Directory of Open Access Journals (Sweden)

    Carlos Alberto da Silva Ledo

    2010-09-01

    Full Text Available The evaluation of the pineapple jelly is intended to analyze the occurrence of syneresis by univariate and multivariate analysis. The jelly of the pineapple presents low concentration pectin, therefore, it was added high methoxyl pectin in the following concentrations: 0.50%, 0.75% and 1.00% corresponding to slow, medium and fast speed of gel formation process. In this study it was checked the pH, acidity, brix and the syneresis of jelly. The highest concentration of pectin in the jelly showed a decrease in the release of the water, syneresis. This result showed that the percentage of 1.00% of pectin in jelly is necessary to form the gel and to obtain a suitable texture.

  20. Kaempferol, a new nutrition-derived pan-inhibitor of human histone deacetylases.

    Science.gov (United States)

    Berger, Alexander; Venturelli, Sascha; Kallnischkies, Mascha; Böcker, Alexander; Busch, Christian; Weiland, Timo; Noor, Seema; Leischner, Christian; Weiss, Thomas S; Lauer, Ulrich M; Bischoff, Stephan C; Bitzer, Michael

    2013-06-01

    Kaempferol is a natural polyphenol belonging to the group of flavonoids. Different biological functions like inhibition of oxidative stress in plants or animal cells and apoptosis induction have been directly associated with kaempferol. The underlying mechanisms are only partially understood. Here we report for the first time that kaempferol has a distinct epigenetic activity by inhibition of histone deacetylases (HDACs). In silico docking analysis revealed that it fits into the binding pocket of HDAC2, 4, 7 or 8 and thereby binds to the zinc ion of the catalytic center. Further in vitro profiling of all conserved human HDACs of class I, II and IV showed that kaempferol inhibited all tested HDACs. In clinical oncology, HDAC inhibitors are currently under investigation as new anticancer compounds. Therefore, we studied the effect of kaempferol on human-derived hepatoma cell lines HepG2 and Hep3B as well as on HCT-116 colon cancer cells and found that it induces hyperacetylation of histone complex H3. Furthermore, kaempferol mediated a prominent reduction of cell viability and proliferation rate. Interestingly, toxicity assays revealed signs of relevant cellular toxicity in primary human hepatocytes only starting at 50 μM as well as in an in vivo chicken embryotoxicity assay at 200 μM. In conclusion, the identification of a novel broad inhibitory capacity of the natural compound kaempferol for human-derived HDAC enzymes opens up the perspective for clinical application in both tumor prevention and therapy. Moreover, kaempferol may serve as a novel lead structure for chemical optimization of pharmacokinetics, pharmacology or inhibitory activities.

  1. A Multivariate Analysis of Extratropical Cyclone Environmental Sensitivity

    Science.gov (United States)

    Tierney, G.; Posselt, D. J.; Booth, J. F.

    2015-12-01

    The implications of a changing climate system include more than a simple temperature increase. A changing climate also modifies atmospheric conditions responsible for shaping the genesis and evolution of atmospheric circulations. In the mid-latitudes, the effects of climate change on extratropical cyclones (ETCs) can be expressed through changes in bulk temperature, horizontal and vertical temperature gradients (leading to changes in mean state winds) as well as atmospheric moisture content. Understanding how these changes impact ETC evolution and dynamics will help to inform climate mitigation and adaptation strategies, and allow for better informed weather emergency planning. However, our understanding is complicated by the complex interplay between a variety of environmental influences, and their potentially opposing effects on extratropical cyclone strength. Attempting to untangle competing influences from a theoretical or observational standpoint is complicated by nonlinear responses to environmental perturbations and a lack of data. As such, numerical models can serve as a useful tool for examining this complex issue. We present results from an analysis framework that combines the computational power of idealized modeling with the statistical robustness of multivariate sensitivity analysis. We first establish control variables, such as baroclinicity, bulk temperature, and moisture content, and specify a range of values that simulate possible changes in a future climate. The Weather Research and Forecasting (WRF) model serves as the link between changes in climate state and ETC relevant outcomes. A diverse set of output metrics (e.g., sea level pressure, average precipitation rates, eddy kinetic energy, and latent heat release) facilitates examination of storm dynamics, thermodynamic properties, and hydrologic cycles. Exploration of the multivariate sensitivity of ETCs to changes in control parameters space is performed via an ensemble of WRF runs coupled with

  2. explorase: Multivariate Exploratory Analysis and Visualization for Systems Biology

    Directory of Open Access Journals (Sweden)

    Michael Lawrence

    2008-03-01

    Full Text Available The datasets being produced by high-throughput biological experiments, such as microarrays, have forced biologists to turn to sophisticated statistical analysis and visualization tools in order to understand their data. We address the particular need for an open-source exploratory data analysis tool that applies numerical methods in coordination with interactive graphics to the analysis of experimental data. The software package, known as explorase, provides a graphical user interface (GUI on top of the R platform for statistical computing and the GGobi software for multivariate interactive graphics. The GUI is designed for use by biologists, many of whom are unfamiliar with the R language. It displays metadata about experimental design and biological entities in tables that are sortable and filterable. There are menu shortcuts to the analysis methods implemented in R, including graphical interfaces to linear modeling tools. The GUI is linked to data plots in GGobi through a brush tool that simultaneously colors rows in the entity information table and points in the GGobi plots.

  3. Cocaine dependence and thalamic functional connectivity: a multivariate pattern analysis

    Directory of Open Access Journals (Sweden)

    Sheng Zhang

    2016-01-01

    Full Text Available Cocaine dependence is associated with deficits in cognitive control. Previous studies demonstrated that chronic cocaine use affects the activity and functional connectivity of the thalamus, a subcortical structure critical for cognitive functioning. However, the thalamus contains nuclei heterogeneous in functions, and it is not known how thalamic subregions contribute to cognitive dysfunctions in cocaine dependence. To address this issue, we used multivariate pattern analysis (MVPA to examine how functional connectivity of the thalamus distinguishes 100 cocaine-dependent participants (CD from 100 demographically matched healthy control individuals (HC. We characterized six task-related networks with independent component analysis of fMRI data of a stop signal task and employed MVPA to distinguish CD from HC on the basis of voxel-wise thalamic connectivity to the six independent components. In an unbiased model of distinct training and testing data, the analysis correctly classified 72% of subjects with leave-one-out cross-validation (p < 0.001, superior to comparison brain regions with similar voxel counts (p < 0.004, two-sample t test. Thalamic voxels that form the basis of classification aggregate in distinct subclusters, suggesting that connectivities of thalamic subnuclei distinguish CD from HC. Further, linear regressions provided suggestive evidence for a correlation of the thalamic connectivities with clinical variables and performance measures on the stop signal task. Together, these findings support thalamic circuit dysfunction in cognitive control as an important neural marker of cocaine dependence.

  4. Multivariate analysis of the globular clusters in M87

    CERN Document Server

    Das, Sukanta; Davoust, Emmanuel

    2015-01-01

    An objective classification of 147 globular clusters in the inner region of the giant elliptical galaxy M87 is carried out with the help of two methods of multivariate analysis. First independent component analysis is used to determine a set of independent variables that are linear combinations of various observed parameters (mostly Lick indices) of the globular clusters. Next K-means cluster analysis is applied on the independent components, to find the optimum number of homogeneous groups having an underlying structure. The properties of the four groups of globular clusters thus uncovered are used to explain the formation mechanism of the host galaxy. It is suggested that M87 formed in two successive phases. First a monolithic collapse, which gave rise to an inner group of metal-rich clusters with little systematic rotation and an outer group of metal-poor clusters in eccentric orbits. In a second phase, the galaxy accreted low-mass satellites in a dissipationless fashion, from the gas of which the two othe...

  5. Multivariate Comparative Analysis of Stock Exchanges: The European Perspective

    Science.gov (United States)

    Koralun-Bereźnicka, Julia

    The aim of the research is to perform a multivariate comparative analysis of 20 European stock exchanges in order to identify the main similarities between the objects. Due to the convergence process of capital markets in Europe the similarities between stock exchanges could be expected to increase over time. The research is meant to show whether and how these similarities change. Consequently, the distances between clusters of similar stock exchanges should become less significant, which the analysis also aims at verifying. The basis of comparison is a set of 48 monthly variables from the period January, 2003 to December, 2006. The variables are classified into three categories: size of the market, equity trading and bonds. The paper aims at identifying the clusters of alike stock exchanges and at finding the characteristic features of each of the distinguished groups. The obtained categorization to some extent corresponds with the division of the European Union into “new” and “old” member countries. Clustering method, performed for each quarter separately, also reveals that the classification is fairly stable in time. The factor analysis, which was carried out to reduce the number of variables, reveals three major factors behind the data, which are related with the earlier mentioned categories of variables.

  6. Treatment and multivariate analysis of colorectal cancer with liver metastasis.

    Science.gov (United States)

    Wang, Yue; Duan, Boshi; Shen, Chunjian; Wu, Bo; Luo, Ji; Zhao, Guohua

    2014-09-01

    The aim of this study was to identify the influencing factors related to outcome of patients of colorectal cancer with liver metastasis. From January 1999 to January 2009, 293 cases of colorectal cancer with liver metastasis undergoing surgery were analysised retrospectively. Relationships between survival and clinicopathological factors including patient demographics and tumor characteristics were evaluated using univariate and multivariate analysis. Results: The 1-, 3- and 5-year survival rates of patients after resection were 58.3%, 26.4%, and 11.3%, respectively. Univariate analysis showed that preoperative CEA level, degree of primary tumor differentiation, resection margin, number of liver metastases, resection of liver metastases were prognostic impacts. The difference was statistically significant (pmultivariate analysis showed that preoperative CEA level, number of liver metastases, and resection of liver metastases are three separate prognostic factors. Racical resection is the key to improve the long-term survival rate of colorectal cancer with liver metastasis. Important predictive factors related to poor survival are preoperative CEA level and number of liver metastases.

  7. Multivariate study and regression analysis of gluten-free granola

    Directory of Open Access Journals (Sweden)

    Lilian Maria Pagamunici

    2014-03-01

    Full Text Available This study developed a gluten-free granola and evaluated it during storage with the application of multivariate and regression analysis of the sensory and instrumental parameters. The physicochemical, sensory, and nutritional characteristics of a product containing quinoa, amaranth and linseed were evaluated. The crude protein and lipid contents ranged from 97.49 and 122.72 g kg-1 of food, respectively. The polyunsaturated/saturated, and n-6:n-3 fatty acid ratios ranged from 2.82 and 2.59:1, respectively. Granola had the best alpha-linolenic acid content, nutritional indices in the lipid fraction, and mineral content. There were good hygienic and sanitary conditions during storage; probably due to the low water activity of the formulation, which contributed to inhibit microbial growth. The sensory attributes ranged from 'like very much' to 'like slightly', and the regression models were highly fitted and correlated during the storage period. A reduction in the sensory attribute levels and in the product physical stabilisation was verified by principal component analysis. The use of the affective test acceptance and instrumental analysis combined with statistical methods allowed us to obtain promising results about the characteristics of gluten-free granola.

  8. Analysis of multivariate extreme intakes of food chemicals

    NARCIS (Netherlands)

    Paulo, M.J.; Voet, van der H.; Wood, J.C.; Marion, G.R.; Klaveren, van J.D.

    2006-01-01

    A recently published multivariate Extreme Value Theory (EVT) model [Heffernan, J.E., Tawn, J.A., 2004. A conditional approach for multivariate extreme values (with discussion). Journal of the Royal Statistical Society Series B 66 (3), 497¿546] is applied to the estimation of population risks associa

  9. Causal diagrams and multivariate analysis II: precision work.

    Science.gov (United States)

    Jupiter, Daniel C

    2014-01-01

    In this Investigators' Corner, I continue my discussion of when and why we researchers should include variables in multivariate regression. My examination focuses on studies comparing treatment groups and situations for which we can either exclude variables from multivariate analyses or include them for reasons of precision. Copyright © 2014 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

  10. Classification of Malaysia aromatic rice using multivariate statistical analysis

    Science.gov (United States)

    Abdullah, A. H.; Adom, A. H.; Shakaff, A. Y. Md; Masnan, M. J.; Zakaria, A.; Rahim, N. A.; Omar, O.

    2015-05-01

    Aromatic rice (Oryza sativa L.) is considered as the best quality premium rice. The varieties are preferred by consumers because of its preference criteria such as shape, colour, distinctive aroma and flavour. The price of aromatic rice is higher than ordinary rice due to its special needed growth condition for instance specific climate and soil. Presently, the aromatic rice quality is identified by using its key elements and isotopic variables. The rice can also be classified via Gas Chromatography Mass Spectrometry (GC-MS) or human sensory panels. However, the uses of human sensory panels have significant drawbacks such as lengthy training time, and prone to fatigue as the number of sample increased and inconsistent. The GC-MS analysis techniques on the other hand, require detailed procedures, lengthy analysis and quite costly. This paper presents the application of in-house developed Electronic Nose (e-nose) to classify new aromatic rice varieties. The e-nose is used to classify the variety of aromatic rice based on the samples odour. The samples were taken from the variety of rice. The instrument utilizes multivariate statistical data analysis, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and K-Nearest Neighbours (KNN) to classify the unknown rice samples. The Leave-One-Out (LOO) validation approach is applied to evaluate the ability of KNN to perform recognition and classification of the unspecified samples. The visual observation of the PCA and LDA plots of the rice proves that the instrument was able to separate the samples into different clusters accordingly. The results of LDA and KNN with low misclassification error support the above findings and we may conclude that the e-nose is successfully applied to the classification of the aromatic rice varieties.

  11. Classification of Malaysia aromatic rice using multivariate statistical analysis

    Energy Technology Data Exchange (ETDEWEB)

    Abdullah, A. H.; Adom, A. H.; Shakaff, A. Y. Md; Masnan, M. J.; Zakaria, A.; Rahim, N. A. [School of Mechatronic Engineering, Universiti Malaysia Perlis, Kampus Pauh Putra, 02600 Arau, Perlis (Malaysia); Omar, O. [Malaysian Agriculture Research and Development Institute (MARDI), Persiaran MARDI-UPM, 43400 Serdang, Selangor (Malaysia)

    2015-05-15

    Aromatic rice (Oryza sativa L.) is considered as the best quality premium rice. The varieties are preferred by consumers because of its preference criteria such as shape, colour, distinctive aroma and flavour. The price of aromatic rice is higher than ordinary rice due to its special needed growth condition for instance specific climate and soil. Presently, the aromatic rice quality is identified by using its key elements and isotopic variables. The rice can also be classified via Gas Chromatography Mass Spectrometry (GC-MS) or human sensory panels. However, the uses of human sensory panels have significant drawbacks such as lengthy training time, and prone to fatigue as the number of sample increased and inconsistent. The GC–MS analysis techniques on the other hand, require detailed procedures, lengthy analysis and quite costly. This paper presents the application of in-house developed Electronic Nose (e-nose) to classify new aromatic rice varieties. The e-nose is used to classify the variety of aromatic rice based on the samples odour. The samples were taken from the variety of rice. The instrument utilizes multivariate statistical data analysis, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and K-Nearest Neighbours (KNN) to classify the unknown rice samples. The Leave-One-Out (LOO) validation approach is applied to evaluate the ability of KNN to perform recognition and classification of the unspecified samples. The visual observation of the PCA and LDA plots of the rice proves that the instrument was able to separate the samples into different clusters accordingly. The results of LDA and KNN with low misclassification error support the above findings and we may conclude that the e-nose is successfully applied to the classification of the aromatic rice varieties.

  12. COMMUNITY VERSUS BIOCOENOSIS IN MULTIVARIATE ANALYSIS OF BENTHIC MOLLUSCAN THANATOCOENOSES

    Directory of Open Access Journals (Sweden)

    DANIELA BASSO

    2002-03-01

    Full Text Available Community and biocoenosis as descriptive units for benthic ecology are not perfectly interchangeable. Although the conceptual framework based on communities, originally defined by a statistical quantitative approach, appears to be the most suitable in the statistical treatment of thanatocoenoses data, this framework appears to oversimplify the picture of the most important ecological units in the Mediterranean benthos. On the contrary, the benthic bionomy with the biocoenoses, identified by a group of characteristic species (disregarding their abundance derives from a qualitative approach which has been more successfully adopted for the research in the Mediterranean area. A group of twelve thanatocoenoses from the Tyrrhenian Sea has been treated with both approaches with the aim to identify a practical strategy for analysing multispecies distribution patterns in benthic paleoecology, trying to combine the advantages of both quantitative and qualitative approaches. When dealing with large-sized data matrices of benthic thanatocoenoses, it is recommended to use a qualitative approach for data reduction, on the basis of their significance in benthic bionomy, prior to perform the quantitative multivariate analysis (classification, ordination, similarity and dissimilarity analysis. This procedure appears to be the most suitable for the identification of “natural” grouping of biotopes, since the results are not obscured by the diffuse occurrence of the most common and ubiquitous species. 

  13. Multivariate analysis of gamma spectra to characterize used nuclear fuel

    Science.gov (United States)

    Coble, Jamie; Orton, Christopher; Schwantes, Jon

    2017-04-01

    The Multi-Isotope Process (MIP) Monitor provides an efficient means to monitor the process conditions in used nuclear fuel reprocessing facilities to support process verification and validation. The MIP Monitor applies multivariate analysis to gamma spectroscopy of key stages in the reprocessing stream in order to detect small changes in the gamma spectrum, which may indicate changes in process conditions. This research extends the MIP Monitor by characterizing a used fuel sample after initial dissolution according to the type of reactor of origin (pressurized or boiling water reactor; PWR and BWR, respectively), initial enrichment, burn up, and cooling time. Simulated gamma spectra were used to develop and test three fuel characterization algorithms. The classification and estimation models employed are based on the partial least squares regression (PLS) algorithm. A PLS discriminate analysis model was developed which perfectly classified reactor type for the three PWR and three BWR reactor designs studied. Locally weighted PLS models were fitted on-the-fly to estimate the remaining fuel characteristics. For the simulated gamma spectra considered, burn up was predicted with 0.1% root mean squared percent error (RMSPE) and both cooling time and initial enrichment with approximately 2% RMSPE. This approach to automated fuel characterization can be used to independently verify operator declarations of used fuel characteristics and to inform the MIP Monitor anomaly detection routines at later stages of the fuel reprocessing stream to improve sensitivity to changes in operational parameters that may indicate issues with operational control or malicious activities.

  14. Multivariate analysis of identity of imported technical PCN formulation.

    Science.gov (United States)

    Falandysz, J; Chudzyński, K; Takekuma, M; Yamamoto, T; Noma, Y; Hanari, N; Yamashita, N

    2008-10-01

    Chloronaphthalenes form a class of compounds consisting of 8 CN homologue groups and altogether of 75 congeners, which used have been most extensively in 1930--1950. An investigation have been performed on the possible origin of unidentified by name technical chloronaphthalene formulation unlawfully imported recently from the United Kingdom to Japan. Principal component analysis (PCA) and Cluster Analysis of chloronaphthalene congener isomer-specific and homologue classes' compositional HRGC/HRMS data of imported CN formulation and of certain brands of technical CN formulation called Halowax (Halowax 1000, 1001 and 1031) enabled to identify that unnamed product is not Halowax 1001. A less accurate multivariate examination based on CN homologue classes patter did indicate on large similarity between unlawfully imported technical CN formulation and Halowax 1001 (manufactured by the Koppers Ind. Co., USA), while a more accurate based on CN congeners pattern differentiated them as to of various origin mixtures. Based on chlorine content of imported CN formulation (50-52%) and its no similarity to Halowax 1001 it seems reasonable to conclude that unnamed CN mixture examined could be a sample of stockpiled Seekay wax R93.

  15. Multivariate analysis of gamma spectra to characterize used nuclear fuel

    Energy Technology Data Exchange (ETDEWEB)

    Coble, Jamie; Orton, Christopher; Schwantes, Jon

    2017-04-01

    Abstract—The Multi-Isotope Process (MIP) Monitor provides an efficient approach to monitoring the process conditions in used nuclear fuel reprocessing facilities to support process verification and validation. The MIP Monitor applies multivariate analysis to gamma spectroscopy of reprocessing streams in order to detect small changes in the gamma spectrum, which may indicate changes in process conditions. This research extends the MIP Monitor by characterizing a used fuel sample after initial dissolution according to the type of reactor of origin (pressurized or boiling water reactor), initial enrichment, burn up, and cooling time. Simulated gamma spectra were used to develop and test three fuel characterization algorithms. The classification and estimation models employed are based on the partial least squares regression (PLS) algorithm. A PLS discriminate analysis model was developed which perfectly classified reactor type. Locally weighted PLS models were fitted on-the-fly to estimate continuous fuel characteristics. Burn up was predicted within 0.1% root mean squared percent error (RMSPE) and both cooling time and initial enrichment within approximately 2% RMSPE. This automated fuel characterization can be used to independently verify operator declarations of used fuel characteristics and inform the MIP Monitor anomaly detection routines at later stages of the fuel reprocessing stream to improve sensitivity to changes in operational parameters and material diversions.

  16. Kaempferol and quercetin glycosides from Rubus idaeus L. leaves.

    Science.gov (United States)

    Gudej, Jan

    2003-01-01

    Quercetin 3-0-beta-D-glucoside (I), quercetin and kaempferol 3-0-beta-D-galactosides (II, III), kaempferol 3-0-beta-L-arabinopyranoside (IV), kaempferol 3-0-beta-D-(6''-E-p-coumaroyl)-glucoside (tiliroside) (V) and methyl gallate (VI) were isolated from Rubus idaeus L. subspecies culture of Norna leaves and fully characterized.

  17. Determining the metabolic footprints of hydrocarbon degradation using multivariate analysis.

    Science.gov (United States)

    Smith, Renee J; Jeffries, Thomas C; Adetutu, Eric M; Fairweather, Peter G; Mitchell, James G

    2013-01-01

    The functional dynamics of microbial communities are largely responsible for the clean-up of hydrocarbons in the environment. However, knowledge of the distinguishing functional genes, known as the metabolic footprint, present in hydrocarbon-impacted sites is still scarcely understood. Here, we conducted several multivariate analyses to characterise the metabolic footprints present in a variety of hydrocarbon-impacted and non-impacted sediments. Non-metric multi-dimensional scaling (NMDS) and canonical analysis of principal coordinates (CAP) showed a clear distinction between the two groups. A high relative abundance of genes associated with cofactors, virulence, phages and fatty acids were present in the non-impacted sediments, accounting for 45.7% of the overall dissimilarity. In the hydrocarbon-impacted sites, a high relative abundance of genes associated with iron acquisition and metabolism, dormancy and sporulation, motility, metabolism of aromatic compounds and cell signalling were observed, accounting for 22.3% of the overall dissimilarity. These results suggest a major shift in functionality has occurred with pathways essential to the degradation of hydrocarbons becoming overrepresented at the expense of other, less essential metabolisms.

  18. Determining the metabolic footprints of hydrocarbon degradation using multivariate analysis.

    Directory of Open Access Journals (Sweden)

    Renee J Smith

    Full Text Available The functional dynamics of microbial communities are largely responsible for the clean-up of hydrocarbons in the environment. However, knowledge of the distinguishing functional genes, known as the metabolic footprint, present in hydrocarbon-impacted sites is still scarcely understood. Here, we conducted several multivariate analyses to characterise the metabolic footprints present in a variety of hydrocarbon-impacted and non-impacted sediments. Non-metric multi-dimensional scaling (NMDS and canonical analysis of principal coordinates (CAP showed a clear distinction between the two groups. A high relative abundance of genes associated with cofactors, virulence, phages and fatty acids were present in the non-impacted sediments, accounting for 45.7% of the overall dissimilarity. In the hydrocarbon-impacted sites, a high relative abundance of genes associated with iron acquisition and metabolism, dormancy and sporulation, motility, metabolism of aromatic compounds and cell signalling were observed, accounting for 22.3% of the overall dissimilarity. These results suggest a major shift in functionality has occurred with pathways essential to the degradation of hydrocarbons becoming overrepresented at the expense of other, less essential metabolisms.

  19. Multivariate analysis in provenance studies: Cerrillos obsidians case, Peru

    Science.gov (United States)

    Bustamante, A.; Delgado, M.; Latini, R. M.; Bellido, A. V. B.

    2007-02-01

    We present the preliminary results of a provenance study of obsidians samples from Cerrillos (ca. 800 100 b.c.) using Mössbauer Spectroscopy. The Cerrillos archaeological site, located in the Upper Ica Valley, Peru, is the only Paracas ceremonial center excavated so far. The archaeological data collected suggest the existence of a complex social and economic organization on the south coast of Peru. Provenance research of obsidian provides valuable information about the selection of lithic resources by our ancestors and eventually about the existence of communication routes and exchange networks. We characterized 18 obsidian artifacts samples by Mössbauer spectroscopy from Cerrillos. The spectra, recorded at room temperature using different velocities, are mainly composed of broad asymmetric doublets due to the superposition of at least two quadrupole doublets corresponding to Fe2+ in two different sites (species A and B), one weak Fe3+ doublet (specie C) and magnetic components associated to the presence of small particles of magnetite. Multivariate statistical analysis of the Mössbauer data (hyperfine parameters) allows to defined two main groups of obsidians, reflecting different geographical origins.

  20. Multivariate analysis in provenance studies: Cerrillos obsidians case, Peru

    Energy Technology Data Exchange (ETDEWEB)

    Bustamante, A., E-mail: abustamanted@unmsm.edu.pe [Universidad Nacional Mayor de San Marcos, Facultad de Ciencias Fisicas (Peru); Delgado, M. [Qallta (Peru); Latini, R. M.; Bellido, A. V. B. [UFF, Instituto de Quimica, Depto. Fisico-Quimica (Brazil)

    2007-02-15

    We present the preliminary results of a provenance study of obsidians samples from Cerrillos (ca. 800-100 b.c.) using Moessbauer Spectroscopy. The Cerrillos archaeological site, located in the Upper Ica Valley, Peru, is the only Paracas ceremonial center excavated so far. The archaeological data collected suggest the existence of a complex social and economic organization on the south coast of Peru. Provenance research of obsidian provides valuable information about the selection of lithic resources by our ancestors and eventually about the existence of communication routes and exchange networks. We characterized 18 obsidian artifacts samples by Moessbauer spectroscopy from Cerrillos. The spectra, recorded at room temperature using different velocities, are mainly composed of broad asymmetric doublets due to the superposition of at least two quadrupole doublets corresponding to Fe{sup 2+} in two different sites (species A and B), one weak Fe{sup 3+} doublet (specie C) and magnetic components associated to the presence of small particles of magnetite. Multivariate statistical analysis of the Moessbauer data (hyperfine parameters) allows to defined two main groups of obsidians, reflecting different geographical origins.

  1. Multivariate analysis of marketing data - applications for bricolage market

    Directory of Open Access Journals (Sweden)

    FANARU Mihai

    2017-01-01

    Full Text Available By using concepts and analytical tools for computing, marketing is directly related to the quantitative methods of economic research and other areas where the efficiency of systems performances are studied. Any activity of the company must be programmed and carried out taking into account the consumer. Providing a complete success in business requires the entrepreneur to see the company and its products through the consumers eyes, to act as representative of its clients in order to acquire and satisfy their desires. Through its complex specific activities, marketing aims to provide goods and services the consumers require or right merchandise in the right quantity at the right price at the right time and place. An important consideration in capturing the link between marketing and multivariate statistical analysis is that it provides more powerful instruments that allow researchers to discover relationships between multiple configurations of the relationship between variables, configurations that would otherwise remain hidden or barely visible. In addition, most methods can do this with good accuracy, with the possibility of testing the statistical significance by calculating the level of confidence associated with the link validation to the entire population and not just the investigated sample.

  2. Atmospheric conditions, lunar phases, and childbirth: a multivariate analysis

    Science.gov (United States)

    Ochiai, Angela Megumi; Gonçalves, Fabio Luiz Teixeira; Ambrizzi, Tercio; Florentino, Lucia Cristina; Wei, Chang Yi; Soares, Alda Valeria Neves; De Araujo, Natalucia Matos; Gualda, Dulce Maria Rosa

    2012-07-01

    Our objective was to assess extrinsic influences upon childbirth. In a cohort of 1,826 days containing 17,417 childbirths among them 13,252 spontaneous labor admissions, we studied the influence of environment upon the high incidence of labor (defined by 75th percentile or higher), analyzed by logistic regression. The predictors of high labor admission included increases in outdoor temperature (odds ratio: 1.742, P = 0.045, 95%CI: 1.011 to 3.001), and decreases in atmospheric pressure (odds ratio: 1.269, P = 0.029, 95%CI: 1.055 to 1.483). In contrast, increases in tidal range were associated with a lower probability of high admission (odds ratio: 0.762, P = 0.030, 95%CI: 0.515 to 0.999). Lunar phase was not a predictor of high labor admission ( P = 0.339). Using multivariate analysis, increases in temperature and decreases in atmospheric pressure predicted high labor admission, and increases of tidal range, as a measurement of the lunar gravitational force, predicted a lower probability of high admission.

  3. Kaempferol and inflammation: From chemistry to medicine.

    Science.gov (United States)

    Devi, Kasi Pandima; Malar, Dicson Sheeja; Nabavi, Seyed Fazel; Sureda, Antoni; Xiao, Jianbo; Nabavi, Seyed Mohammad; Daglia, Maria

    2015-09-01

    Inflammation is an important process of human healing response, wherein the tissues respond to injuries induced by many agents including pathogens. It is characterized by pain, redness and heat in the injured tissues. Chronic inflammation seems to be associated with different types of diseases such as arthritis, allergies, atherosclerosis, and even cancer. In recent years natural product based drugs are considered as the novel therapeutic strategy for prevention and treatment of inflammatory diseases. Among the different types of phyto-constituents present in natural products, flavonoids which occur in many vegetable foods and herbal medicines are considered as the most active constituent, which has the potency to ameliorate inflammation under both in vitro and in vivo conditions. Kaempferol is a natural flavonol present in different plant species, which has been described to possess potent anti-inflammatory properties. Despite the voluminous literature on the anti-inflammatory effects of kaempferol, only very limited review articles has been published on this topic. Hence the present review is aimed to provide a critical overview on the anti-inflammatory effects and the mechanisms of action of kaempferol, based on the current scientific literature. In addition, emphasis is also given on the chemistry, natural sources, bioavailability and toxicity of kaempferol.

  4. Multivariate factor analysis of Girgentana goat milk composition

    Directory of Open Access Journals (Sweden)

    Pietro Giaccone

    2010-01-01

    Full Text Available The interpretation of the several variables that contribute to defining milk quality is difficult due to the high degree of  correlation among them. In this case, one of the best methods of statistical processing is factor analysis, which belongs  to the multivariate groups; for our study this particular statistical approach was employed.  A total of 1485 individual goat milk samples from 117 Girgentana goats, were collected fortnightly from January to July,  and analysed for physical and chemical composition, and clotting properties. Milk pH and tritable acidity were within the  normal range for fresh goat milk. Morning milk yield resulted 704 ± 323 g with 3.93 ± 1.23% and 3.48±0.38% for fat  and protein percentages, respectively. The milk urea content was 43.70 ± 8.28 mg/dl. The clotting ability of Girgentana  milk was quite good, with a renneting time equal to 16.96 ± 3.08 minutes, a rate of curd formation of 2.01 ± 1.63 min-  utes and a curd firmness of 25.08 ± 7.67 millimetres.  Factor analysis was performed by applying axis orthogonal rotation (rotation type VARIMAX; the analysis grouped the  milk components into three latent or common factors. The first, which explained 51.2% of the total covariance, was  defined as “slow milks”, because it was linked to r and pH. The second latent factor, which explained 36.2% of the total  covariance, was defined as “milk yield”, because it is positively correlated to the morning milk yield and to the urea con-  tent, whilst negatively correlated to the fat percentage. The third latent factor, which explained 12.6% of the total covari-  ance, was defined as “curd firmness,” because it is linked to protein percentage, a30 and titatrable acidity. With the aim  of evaluating the influence of environmental effects (stage of kidding, parity and type of kidding, factor scores were anal-  ysed with the mixed linear model. Results showed significant effects of the season of

  5. Kaempferol and Kaempferol Rhamnosides with Depigmenting and Anti-Inflammatory Properties

    OpenAIRE

    Jae Youl Cho; Dong Ha Cho; Keun Ha Lee; Sun Sang Kwon; Dae Sung Yoo; Soo Mi Ahn; Amal Kumar Ghimeray; Ho Sik Rho

    2011-01-01

    The objective of this study was to examine the biological activity of kaempferol and its rhamnosides. We isolated kaempferol (1), a-rhamnoisorobin (2), afzelin (3), and kaempferitrin (4) as pure compounds by far-infrared (FIR) irradiation of kenaf (Hibiscus cannabinus L.) leaves. The depigmenting and anti-inflammatory activity of the compounds was evaluated by analyzing their structure-activity relationships. The order of the inhibitory activity with regard to depigmentation and nitric oxide ...

  6. Deeper Insights into the Circumgalactic Medium using Multivariate Analysis Methods

    Science.gov (United States)

    Lewis, James; Churchill, Christopher W.; Nielsen, Nikole M.; Kacprzak, Glenn

    2017-01-01

    Drawing from a database of galaxies whose surrounding gas has absorption from MgII, called the MgII-Absorbing Galaxy Catalog (MAGIICAT, Neilsen et al 2013), we studied the circumgalactic medium (CGM) for a sample of 47 galaxies. Using multivariate analysis, in particular the k-means clustering algorithm, we determined that simultaneously examining column density (N), rest-frame B-K color, virial mass, and azimuthal angle (the projected angle between the galaxy major axis and the quasar line of sight) yields two distinct populations: (1) bluer, lower mass galaxies with higher column density along the minor axis, and (2) redder, higher mass galaxies with lower column density along the major axis. We support this grouping by running (i) two-sample, two-dimensional Kolmogorov-Smirnov (KS) tests on each of the six bivariate planes and (ii) two-sample KS tests on each of the four variables to show that the galaxies significantly cluster into two independent populations. To account for the fact that 16 of our 47 galaxies have upper limits on N, we performed Monte-Carlo tests whereby we replaced upper limits with random deviates drawn from a Schechter distribution fit, f(N). These tests strengthen the results of the KS tests. We examined the behavior of the MgII λ2796 absorption line equivalent width and velocity width for each galaxy population. We find that equivalent width and velocity width do not show similar characteristic distinctions between the two galaxy populations. We discuss the k-means clustering algorithm for optimizing the analysis of populations within datasets as opposed to using arbitrary bivariate subsample cuts. We also discuss the power of the k-means clustering algorithm in extracting deeper physical insight into the CGM in relationship to host galaxies.

  7. In Vivo Exposure of Kaempferol Is Driven by Phase II Metabolic Enzymes and Efflux Transporters.

    Science.gov (United States)

    Zheng, Liang; Zhu, Lijun; Zhao, Min; Shi, Jian; Li, Yuhuan; Yu, Jia; Jiang, Huangyu; Wu, Jinjun; Tong, Yunli; Liu, Yuting; Hu, Ming; Lu, Linlin; Liu, Zhongqiu

    2016-09-01

    Kaempferol is a well-known flavonoid; however, it lacks extensive pharmacokinetic studies. Phase II metabolic enzymes and efflux transporters play an important role in the disposition of flavonoids. This study aimed to investigate the mechanism by which phase II metabolic enzymes and efflux transporters determine the in vivo exposure of kaempferol. Pharmacokinetic analysis in Sprague-Dawley rats revealed that kaempferol was mostly biotransformed to conjugates, namely, kaempferol-3-glucuronide (K-3-G), kaempferol-7-glucuronide (K-7-G), and kaempferol-7-sulfate, in plasma. K-3-G represented the major metabolite. Compared with that in wild-type mice, pharmacokinetics in knockout FVB mice demonstrated that the absence of multidrug resistance protein 2 (MRP2) and breast cancer resistance protein (BCRP) significantly increased the area under the curve (AUC) of the conjugates. The lack of MRP1 resulted in a much lower AUC of the conjugates. Intestinal perfusion in rats revealed that the glucuronide conjugates were mainly excreted in the small intestine, but 7-sulfate was mainly excreted in the colon. In Caco-2 monolayers, K-7-G efflux toward the apical (AP) side was significantly higher than K-3-G efflux. In contrast, K-3-G efflux toward the basolateral (BL) side was significantly higher than K-7-G efflux. The BL-to-AP efflux was significantly reduced in the presence of the MRP2 inhibitor LTC4. The AP-to-BL efflux was significantly decreased in the presence of the BL-side MRPs inhibitor MK571. The BCRP inhibitor Ko143 decreased the glucuronide conjugate efflux. Therefore, kaempferol is mainly exposed as K-3-G in vivo, which is driven by phase II metabolic enzymes and efflux transporters (i.e., BCRP and MRPs).

  8. Kaempferol targets RSK2 and MSK1 to suppress UV radiation-induced skin cancer.

    Science.gov (United States)

    Yao, Ke; Chen, Hanyong; Liu, Kangdong; Langfald, Alyssa; Yang, Ge; Zhang, Yi; Yu, Dong Hoon; Kim, Myoung Ok; Lee, Mee-Hyun; Li, Haitao; Bae, Ki Beom; Kim, Hong-Gyum; Ma, Wei-Ya; Bode, Ann M; Dong, Ziming; Dong, Zigang

    2014-09-01

    Solar UV (SUV) irradiation is a major factor in skin carcinogenesis, the most common form of cancer in the United States. The MAPK cascades are activated by SUV irradiation. The 90 kDa ribosomal S6 kinase (RSK) and mitogen and stress-activated protein kinase (MSK) proteins constitute a family of protein kinases that mediate signal transduction downstream of the MAPK cascades. In this study, phosphorylation of RSK and MSK1 was upregulated in human squamous cell carcinoma (SCC) and SUV-treated mouse skin. Kaempferol, a natural flavonol, found in tea, broccoli, grapes, apples, and other plant sources, is known to have anticancer activity, but its mechanisms and direct target(s) in cancer chemoprevention are unclear. Kinase array results revealed that kaempferol inhibited RSK2 and MSK1. Pull-down assay results, ATP competition, and in vitro kinase assay data revealed that kaempferol interacts with RSK2 and MSK1 at the ATP-binding pocket and inhibits their respective kinase activities. Mechanistic investigations showed that kaempferol suppresses RSK2 and MSK1 kinase activities to attenuate SUV-induced phosphorylation of cAMP-responsive element binding protein (CREB) and histone H3 in mouse skin cells. Kaempferol was a potent inhibitor of SUV-induced mouse skin carcinogenesis. Further analysis showed that skin from the kaempferol-treated group exhibited a substantial reduction in SUV-induced phosphorylation of CREB, c-Fos, and histone H3. Overall, our results identify kaempferol as a safe and novel chemopreventive agent against SUV-induced skin carcinogenesis that acts by targeting RSK2 and MSK1.

  9. MULTIVARIATE ANALYSIS OF BONE METASTASES IN BREAST CARCINOMA

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    Objective: To investigate the risk factors of bone metastases in breast carcinoma. Methods: By cross sectional study, the data of 225 breast cancer patients who were inpatients in four hospitals in Hangzhou were analyzed. All patients underwent total body bone scan with single photon emission computed tomography (SPECT) at least once during 1995 to 2000. Results: All patients were followed-up to 294 months after operation, bone metastases were found in 113 cases, suspected bone metastases 3 cases, with a bone metastases rate of 50.9% (113/222). Multivariate analysis by Cox's proportional hazards regression model showed that there were four risk factors of bone metastases in breast cancer: (1) clinical stage, I(IV stages with a hazard ratio of bone metastases of 1.945, 95% confidence interval 1.396(2.710; (2) number of invaded axillary lymph nodes, with a hazard ratio of 1.039, 95% confidence interval 1.0142(1.068; (3) skeletal complications (yes vs. no), with a hazard ratio of bone metastases of 1.722, 95% confidence interval 1.060(2.796; (4) age at the time of surgery or diagnosis, with a hazard ratio of 2.048, 95% confidence interval 1.123(3.876 for patients of age 40(50 y versus patients bellow 40 y of age and 2.837, 95% confidence interval 1.473(5.465 for patients of age above 50 y versus patients of ages between 40 and 50. Kaplan-Meier curves showed that for patients with more than 5 invasive axillary lymph nodes, compared with those with 1(5, the bone metastasis rates increased significantly ((2 =6.3319, P=0.012). Conclusion: The clinical stage, number of metastatic axillary lymph nodes, age at the time of operation and skeletal complications are essential risk factors of bone metastases.

  10. Statistical analysis of multivariate atmospheric variables. [cloud cover

    Science.gov (United States)

    Tubbs, J. D.

    1979-01-01

    Topics covered include: (1) estimation in discrete multivariate distributions; (2) a procedure to predict cloud cover frequencies in the bivariate case; (3) a program to compute conditional bivariate normal parameters; (4) the transformation of nonnormal multivariate to near-normal; (5) test of fit for the extreme value distribution based upon the generalized minimum chi-square; (6) test of fit for continuous distributions based upon the generalized minimum chi-square; (7) effect of correlated observations on confidence sets based upon chi-square statistics; and (8) generation of random variates from specified distributions.

  11. Application of multivariate statistical analysis to STEM X-ray spectral images: interfacial analysis in microelectronics.

    Science.gov (United States)

    Kotula, Paul G; Keenan, Michael R

    2006-12-01

    Multivariate statistical analysis methods have been applied to scanning transmission electron microscopy (STEM) energy-dispersive X-ray spectral images. The particular application of the multivariate curve resolution (MCR) technique provides a high spectral contrast view of the raw spectral image. The power of this approach is demonstrated with a microelectronics failure analysis. Specifically, an unexpected component describing a chemical contaminant was found, as well as a component consistent with a foil thickness change associated with the focused ion beam specimen preparation process. The MCR solution is compared with a conventional analysis of the same spectral image data set.

  12. Metabolomic profiling of the phytomedicinal constituents of Carica papaya L. leaves and seeds by 1H NMR spectroscopy and multivariate statistical analysis.

    Science.gov (United States)

    Gogna, Navdeep; Hamid, Neda; Dorai, Kavita

    2015-11-10

    Extracts from the Carica papaya L. plant are widely reported to contain metabolites with antibacterial, antioxidant and anticancer activity. This study aims to analyze the metabolic profiles of papaya leaves and seeds in order to gain insights into their phytomedicinal constituents. We performed metabolite fingerprinting using 1D and 2D 1H NMR experiments and used multivariate statistical analysis to identify those plant parts that contain the most concentrations of metabolites of phytomedicinal value. Secondary metabolites such as phenyl propanoids, including flavonoids, were found in greater concentrations in the leaves as compared to the seeds. UPLC-ESI-MS verified the presence of significant metabolites in the papaya extracts suggested by the NMR analysis. Interestingly, the concentration of eleven secondary metabolites namely caffeic, cinnamic, chlorogenic, quinic, coumaric, vanillic, and protocatechuic acids, naringenin, hesperidin, rutin, and kaempferol, were higher in young as compared to old papaya leaves. The results of the NMR analysis were corroborated by estimating the total phenolic and flavonoid content of the extracts. Estimation of antioxidant activity in leaves and seed extracts by DPPH and ABTS in-vitro assays and antioxidant capacity in C2C12 cell line also showed that papaya extracts exhibit high antioxidant activity.

  13. Multivariate Stable Isotope Analysis to Determine Linkages between Benzocaine Seizures

    Science.gov (United States)

    Kemp, H. F.; Meier-Augenstein, W.; Collins, M.; Salouros, H.; Cunningham, A.; Harrison, M.

    2012-04-01

    In July 2010, a woman was jailed for nine years in the UK after the prosecution successfully argued that attempting to import a cutting agent was proof of involvement in a conspiracy to supply Cocaine. That landmark ruling provided law enforcement agencies with much greater scope to tackle those involved in this aspect of the drug trade, specifically targeting those importing the likes of benzocaine or lidocaine. Huge quantities of these compounds are imported into the UK and between May and August 2010, four shipments of Benzocaine amounting to more then 4 tons had been seized as part of Operation Kitley, a joint initiative between the UK Border Agency and the Serious Organised Crime Agency (SOCA). By diluting cocaine, traffickers can make it go a lot further for very little cost, leading to huge profits. In recent years, dealers have moved away from inert substances, like sugar and baby milk powder, in favour of active pharmaceutical ingredients (APIs), including anaesthetics like Benzocaine and Lidocaine. Both these mimic the numbing effect of cocaine, and resemble it closely in colour, texture and some chemical behaviours, making it easier to conceal the fact that the drug has been diluted. API cutting agents have helped traffickers to maintain steady supplies in the face of successful interdiction and even expand the market in the UK, particularly to young people aged from their mid teens to early twenties. From importation to street-level, the purity of the drug can be reduced up to a factor of 80 and street level cocaine can have a cocaine content as low as 1%. In view of the increasing use of Benzocaine as cutting agent for Cocaine, a study was carried out to investigate if 2H, 13C, 15N and 18O stable isotope signatures could be used in conjunction with multivariate chemometric data analysis to determine potential linkage between benzocaine exhibits seized from different locations or individuals to assist with investigation and prosecution of drug

  14. A new kaempferol trioside from Silphium perfoliatum.

    Science.gov (United States)

    Feng, Wei-Sheng; Pei, Yuan-Yuan; Zheng, Xiao-Ke; Li, Chun-Ge; Ke, Ying-Ying; Lv, Yan-Yan; Zhang, Yan-Li

    2014-01-01

    A new apiose-containing kaempferol trioside, kaempferol-3-O-α-L-rhamnosyl-(1‴ → 6″)-O-β-D-galactopyranosyl-7-O-β-D-apiofuranoside, along with 16 known compounds, were isolated from 50% acetone extract of Silphium perfoliatum L. Their structures were elucidated by acid hydrolysis and spectroscopic techniques including UV, IR, MS, ¹H, ¹³C, and 2D-NMR. In addition, the pharmacological activity of compound 1 was tested with HepG2 and Balb/c mice (splenic lymphocytes and thymic lymphocytes) in vitro, and it exhibited inhibitory effect on the proliferation of HepG2 cells and showed the immunosuppressive activity.

  15. A Multivariate Analysis of Risk Factors for Diabetic Nephropathy

    Directory of Open Access Journals (Sweden)

    Anthony Shannon

    2007-03-01

    Full Text Available This paper uses multivariate methods on actual data from 267 patients with noninsulin- dependent (Type 2 diabetes mellitus in order to see how the various risk factors can affect the progression of diabetic nephropathy. The approach succeeds in identifying preliminary risk factors such as smoking for males, although the females had higher fasting blood glucose at diagnosis. Not surprisingly, hypertension is common among patients of both sexes and it has an association with proteinuria in female patients in the sample.

  16. Causal diagrams and multivariate analysis III: confound it!

    Science.gov (United States)

    Jupiter, Daniel C

    2015-01-01

    This commentary concludes my series concerning inclusion of variables in multivariate analyses. We take up the issues of confounding and effect modification and summarize the work we have thus far done. Finally, we provide a rough algorithm to help guide us through the maze of possibilities that we have outlined. Copyright © 2015 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

  17. Analysis of Forest Foliage Using a Multivariate Mixture Model

    Science.gov (United States)

    Hlavka, C. A.; Peterson, David L.; Johnson, L. F.; Ganapol, B.

    1997-01-01

    Data with wet chemical measurements and near infrared spectra of ground leaf samples were analyzed to test a multivariate regression technique for estimating component spectra which is based on a linear mixture model for absorbance. The resulting unmixed spectra for carbohydrates, lignin, and protein resemble the spectra of extracted plant starches, cellulose, lignin, and protein. The unmixed protein spectrum has prominent absorption spectra at wavelengths which have been associated with nitrogen bonds.

  18. Correlative and multivariate analysis of increased radon concentration in underground laboratory.

    Science.gov (United States)

    Maletić, Dimitrije M; Udovičić, Vladimir I; Banjanac, Radomir M; Joković, Dejan R; Dragić, Aleksandar L; Veselinović, Nikola B; Filipović, Jelena

    2014-11-01

    The results of analysis using correlative and multivariate methods, as developed for data analysis in high-energy physics and implemented in the Toolkit for Multivariate Analysis software package, of the relations of the variation of increased radon concentration with climate variables in shallow underground laboratory is presented. Multivariate regression analysis identified a number of multivariate methods which can give a good evaluation of increased radon concentrations based on climate variables. The use of the multivariate regression methods will enable the investigation of the relations of specific climate variable with increased radon concentrations by analysis of regression methods resulting in 'mapped' underlying functional behaviour of radon concentrations depending on a wide spectrum of climate variables. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  19. Publishing nutrition research: a review of multivariate techniques--part 2: analysis of variance.

    Science.gov (United States)

    Harris, Jeffrey E; Sheean, Patricia M; Gleason, Philip M; Bruemmer, Barbara; Boushey, Carol

    2012-01-01

    This article is the eighth in a series exploring the importance of research design, statistical analysis, and epidemiology in nutrition and dietetics research, and the second in a series focused on multivariate statistical analytical techniques. The purpose of this review is to examine the statistical technique, analysis of variance (ANOVA), from its simplest to multivariate applications. Many dietetics practitioners are familiar with basic ANOVA, but less informed of the multivariate applications such as multiway ANOVA, repeated-measures ANOVA, analysis of covariance, multiple ANOVA, and multiple analysis of covariance. The article addresses all these applications and includes hypothetical and real examples from the field of dietetics.

  20. Analysis techniques for multivariate root loci. [a tool in linear control systems

    Science.gov (United States)

    Thompson, P. M.; Stein, G.; Laub, A. J.

    1980-01-01

    Analysis and techniques are developed for the multivariable root locus and the multivariable optimal root locus. The generalized eigenvalue problem is used to compute angles and sensitivities for both types of loci, and an algorithm is presented that determines the asymptotic properties of the optimal root locus.

  1. A Primer on Multivariate Analysis of Variance (MANOVA) for Behavioral Scientists

    Science.gov (United States)

    Warne, Russell T.

    2014-01-01

    Reviews of statistical procedures (e.g., Bangert & Baumberger, 2005; Kieffer, Reese, & Thompson, 2001; Warne, Lazo, Ramos, & Ritter, 2012) show that one of the most common multivariate statistical methods in psychological research is multivariate analysis of variance (MANOVA). However, MANOVA and its associated procedures are often not…

  2. Identification of Homogeneous Hydrological Regions through Multivariate Analysis

    Directory of Open Access Journals (Sweden)

    Álvarez-Olguín G.

    2011-07-01

    Full Text Available Hydrological regionalization is used to transfer information from gauged catchments to ungauged river basins. However, to obtain reliable results, the basins involved must have a similar hydrological behavior. The objective of this research was to identify hydrologically homogeneous regions in the Mixteca Oaxaqueña and surrounding areas. The area of study included 17 basins for which 20 climate and physiographic variables potentially useful in the prediction of flow were quantified. The applications of multivariate statistics techniques allowed us to identify three groups of basins hydrologically associated. A regional model was obtained to predict mean annual fl ow, which determined that the best predictive variables are the area and the average annual precipitation.

  3. Inhibitory effects of kaempferol on the invasion of human breast carcinoma cells by downregulating the expression and activity of matrix metalloproteinase-9.

    Science.gov (United States)

    Li, Chenglin; Zhao, Yuanwei; Yang, Dan; Yu, Yanyan; Guo, Hao; Zhao, Ziming; Zhang, Bei; Yin, Xiaoxing

    2015-02-01

    Matrix metalloproteinases (MMPs) have been regarded as major critical molecules assisting tumor cells during metastasis, for excessive ECM (ECM) degradation, and cancer cell invasion. In the present study, in vitro and in vivo assays were employed to examine the inhibitory effects of kaempferol, a natural polyphenol of flavonoid family, on tumor metastasis. Data showed that kaempferol could inhibit adhesion, migration, and invasion of MDA-MB-231 human breast carcinoma cells. Moreover, kaempferol led to the reduced activity and expression of MMP-2 and MMP-9, which were detected by gelatin zymography, real-time PCR, and western blot analysis, respectively. Further elucidation of the mechanism revealed that kaempferol treatment inhibited the activation of transcription factor activator protein-1 (AP-1) and MAPK signaling pathway. Moreover, kaempferol repressed phorbol-12-myristate-13-acetate (PMA)-induced MMP-9 expression and activity through suppressing the translocation of protein kinase Cδ (PKCδ) and MAPK signaling pathway. Our results also indicated that kaempferol could block the lung metastasis of B16F10 murine melanoma cells as well as the expression of MMP-9 in vivo. Taken together, these results demonstrated that kaempferol could inhibit cancer cell invasion through blocking the PKCδ/MAPK/AP-1 cascade and subsequent MMP-9 expression and its activity. Therefore, kaempferol might act as a therapeutic potential candidate for cancer metastasis.

  4. Changes in cod muscle proteins during frozen storage revealed by proteome analysis and multivariate data analysis

    DEFF Research Database (Denmark)

    Kjærsgård, Inger Vibeke Holst; Nørrelykke, M.R.; Jessen, Flemming

    2006-01-01

    Multivariate data analysis has been combined with proteomics to enhance the recovery of information from 2-DE of cod muscle proteins during different storage conditions. Proteins were extracted according to 11 different storage conditions and samples were resolved by 2-DE. Data generated by 2-DE...... was subjected to principal component analysis (PCA) and discriminant partial least squares regression (DPLSR). Applying PCA to 2-DE data revealed the samples to form groups according to frozen storage time, whereas differences due to different storage temperatures or chilled storage in modified atmosphere...... light chain 1, 2 and 3, triose-phosphate isomerase, glyceraldehyde-3-phosphate dehydrogenase, aldolase A and two ?-actin fragments, and a nuclease diphosphate kinase B fragment to change in concentration, during frozen storage. Application of proteomics, multivariate data analysis and MS/MS to analyse...

  5. Principal Feature Analysis: A Multivariate Feature Selection Method for fMRI Data

    Directory of Open Access Journals (Sweden)

    Lijun Wang

    2013-01-01

    Full Text Available Brain decoding with functional magnetic resonance imaging (fMRI requires analysis of complex, multivariate data. Multivoxel pattern analysis (MVPA has been widely used in recent years. MVPA treats the activation of multiple voxels from fMRI data as a pattern and decodes brain states using pattern classification methods. Feature selection is a critical procedure of MVPA because it decides which features will be included in the classification analysis of fMRI data, thereby improving the performance of the classifier. Features can be selected by limiting the analysis to specific anatomical regions or by computing univariate (voxel-wise or multivariate statistics. However, these methods either discard some informative features or select features with redundant information. This paper introduces the principal feature analysis as a novel multivariate feature selection method for fMRI data processing. This multivariate approach aims to remove features with redundant information, thereby selecting fewer features, while retaining the most information.

  6. MULTIVARIABLE ANALYSIS OF 2,4-D HERBICIDE PHOTOCATALYTIC DEGRADATION

    Directory of Open Access Journals (Sweden)

    ANDRÉS F. LÓPEZ-VÁSQUEZ

    2011-01-01

    Full Text Available La degradación del herbicida 2,4-D en suspensiones de TiO2 en agua real fue evaluada bajo condiciones de irradiación artificial. El análisis multivariable de metodología de superficie de respuesta (MSR, se aplicó para evaluar el efecto de variables como la concentración de catalizador y pesticida, el pH y el caudal volumétrico sobre la reacción fotocatalítica en dos fotorreactores catalíticos: placa plana y tubular. La variable de respuesta fue la mineralización del pesticida expresada como porcentaje de degradación de carbono orgánico total (COT después de cuatro horas de irradiación. Para el fotorreactor tubular, los cuatro factores tuvieron la misma significancia sobre la degradación, mientras que para el fotorreactor de placa plana inclinada, sólo la concentración de catalizador y el pH tuvieron significancia. La MSR fue una técnica adecuada para obtener parámetros de operación óptimos de un proceso fotocatalítico con un reactor específico y dentro de un rango de estudio determinado.

  7. Suicidal ideation among Canadian youth: a multivariate analysis.

    Science.gov (United States)

    Peter, Tracey; Roberts, Lance W; Buzdugan, Raluca

    2008-01-01

    A multivariate model was developed incorporating various socio-demographic, social-environmental, and social-psychological factors in an attempt to predict suicidal ideation among Canadian youth. The main research objective sought to determine what socially based factors elevate or reduce suicidal ideation within this population. Using data from the National Longitudinal Study of Children and Youth-Cycle 5 (2003), a cross-sectional sample of 1,032 was used to empirically identify various social determinants of suicidal ideation among youth between the ages of 12 and 15. Results reveal statistically significant correlations between suicide ideation and some lesser examined socially based measures. In particular, ability to communicate feelings, negative attachment to parents/guardians, taunting/bullying or abuse, and presence of deviant peers were significant predictors of suicidal ideation. As expected, depression/anxiety, gender, and age were also correlated with thoughts of suicide. Research findings should help foster a better understanding toward the social elements of suicide and provide insight into how suicide prevention strategies may be improved through an increased emphasis on substance use education, direct targeting of dysfunctional families and deviant peer groups, and exploring more avenues of self-expression for youth.

  8. [Application of multivariate statistical analysis and thinking in quality control of Chinese medicine].

    Science.gov (United States)

    Liu, Na; Li, Jun; Li, Bao-Guo

    2014-11-01

    The study of quality control of Chinese medicine has always been the hot and the difficulty spot of the development of traditional Chinese medicine (TCM), which is also one of the key problems restricting the modernization and internationalization of Chinese medicine. Multivariate statistical analysis is an analytical method which is suitable for the analysis of characteristics of TCM. It has been used widely in the study of quality control of TCM. Multivariate Statistical analysis was used for multivariate indicators and variables that appeared in the study of quality control and had certain correlation between each other, to find out the hidden law or the relationship between the data can be found,.which could apply to serve the decision-making and realize the effective quality evaluation of TCM. In this paper, the application of multivariate statistical analysis in the quality control of Chinese medicine was summarized, which could provided the basis for its further study.

  9. Using multivariate statistical analysis to assess changes in water ...

    African Journals Online (AJOL)

    analysis (CCA) showed that the environmental variables used in the analysis, discharge and month of ... International studies with regard to impacts on aquatic systems .... frequently used to assess for the impact of acidic deposition on.

  10. Analysis/forecast experiments with a multivariate statistical analysis scheme using FGGE data

    Science.gov (United States)

    Baker, W. E.; Bloom, S. C.; Nestler, M. S.

    1985-01-01

    A three-dimensional, multivariate, statistical analysis method, optimal interpolation (OI) is described for modeling meteorological data from widely dispersed sites. The model was developed to analyze FGGE data at the NASA-Goddard Laboratory of Atmospherics. The model features a multivariate surface analysis over the oceans, including maintenance of the Ekman balance and a geographically dependent correlation function. Preliminary comparisons are made between the OI model and similar schemes employed at the European Center for Medium Range Weather Forecasts and the National Meteorological Center. The OI scheme is used to provide input to a GCM, and model error correlations are calculated for forecasts of 500 mb vertical water mixing ratios and the wind profiles. Comparisons are made between the predictions and measured data. The model is shown to be as accurate as a successive corrections model out to 4.5 days.

  11. TMVA(Toolkit for Multivariate Analysis) new architectures design and implementation.

    CERN Document Server

    Zapata Mesa, Omar Andres

    2016-01-01

    Toolkit for Multivariate Analysis(TMVA) is a package in ROOT for machine learning algorithms for classification and regression of the events in the detectors. In TMVA, we are developing new high level algorithms to perform multivariate analysis as cross validation, hyper parameter optimization, variable importance etc... Almost all the algorithms are expensive and designed to process a huge amount of data. It is very important to implement the new technologies on parallel computing to reduce the processing times.

  12. Analysis of the real EADGENE data set::Multivariate approaches and post analysis

    OpenAIRE

    Schuberth Hans-Joachim; van Schothorst Evert M; Lund Mogens; San Cristobal Magali; Robert-Granié Christèle; Pool Marco H; Petzl Wolfram; Nie Haisheng; Cao Kim-Anh; de Koning Dirk-Jan; Jiang Li; Jensen Kirsty; Hulsegge Ina; Jaffrézic Florence; Hornshøj Henrik

    2007-01-01

    Abstract The aim of this paper was to describe, and when possible compare, the multivariate methods used by the participants in the EADGENE WP1.4 workshop. The first approach was for class discovery and class prediction using evidence from the data at hand. Several teams used hierarchical clustering (HC) or principal component analysis (PCA) to identify groups of differentially expressed genes with a similar expression pattern over time points and infective agent (E. coli or S. aureus). The m...

  13. A brief introduction to multivariate methods in grape and wine analysis

    Directory of Open Access Journals (Sweden)

    D Cozzolino

    2009-03-01

    Full Text Available D Cozzolino1, W U Cynkar1, N Shah1, R G Dambergs2, P A Smith11The Australian Wine Research Institute, Urrbrae, Glen Osmond, SA, Australia; 2The Australian Wine Research Institute, Tasmanian Institute of Agricultural Research, University of Tasmania, Hobart, Tasmania, AustraliaAbstract: Real-world systems are usually multivariate and hence usually cannot be adequately described by one selected variable without the risk of serious misrepresentation. Analyzing the effect of one variable at a time by analysis of variance techniques can give useful descriptive information, but this will not give specific information about relationships among variables and other important relationships in the entire matrix. Multivariate data analysis was developed in the late 1960s, and used by a number of research groups in analytical and physical organic chemistry due to the introduction of instrumentation giving multivariate responses for each sample analyzed. Development of such methods was also made possible by the availability of computers. Multivariate data analysis involves the use of mathematical and statistical techniques to extract information from complex data sets. The objective of this paper is to briefly describe and illustrate some multivariate data analysis methods used for grape and wine analysis.Keywords: multivariate analysis, data mining, wine, grape 

  14. explorase: Multivariate Exploratory Analysis and Visualization for Systems Biology

    OpenAIRE

    2008-01-01

    The datasets being produced by high-throughput biological experiments, such as microarrays, have forced biologists to turn to sophisticated statistical analysis and visualization tools in order to understand their data. We address the particular need for an open-source exploratory data analysis tool that applies numerical methods in coordination with interactive graphics to the analysis of experimental data. The software package, known as explorase, provides a graphical user interface (GUI) ...

  15. Application of Multivariable Statistical Techniques in Plant-wide WWTP Control Strategies Analysis

    DEFF Research Database (Denmark)

    Flores Alsina, Xavier; Comas, J.; Rodríguez-Roda, I.

    2007-01-01

    The main objective of this paper is to present the application of selected multivariable statistical techniques in plant-wide wastewater treatment plant (WWTP) control strategies analysis. In this study, cluster analysis (CA), principal component analysis/factor analysis (PCA/FA) and discriminant...

  16. HPLC identification and determination of myricetin, quercetin, kaempferol and total flavonoids in herbal drugs

    Directory of Open Access Journals (Sweden)

    Svetlana Kulevanova

    2003-05-01

    Full Text Available A new and rapid HPLC method for identification and determination of myricetin, quercetin, kaempferol and total flavonoids in ten herbal drugs of Macedonian origin is presented. Preparation of samples (Uvae ursi folim, Pruni spinosae flos, Sambuci flos, Betulae folim, Primulae flos, Herniariae herba, Centaurii herba, Tiliae flos, Robiniae pseudoacaciae flos, Bursae pastoris herba included hydrolysis of glycosides and extraction of total aglycones with ethyl acetate. HPLC analysis with UV-diode array detection was carried out on RP C18 column, using 5% acetic acid and acetonitrile in agradient elution mode and column temperature of 30 o C. The monitoring of the elution is performed in the whole UV-range and the acquisition of data for quantitative analysis at 367 nm. Screening of the extracts showed presence of quercetin in nine, kaempferol in seven and myricetin in only one sample. The quantitative analysis showed that the content of quercetin ranged from 0.026-0.506 % (m/m, while for kaempferol it was from traces to 1.246 %. Uvaeursi folium and Pruni spinosae flos were rich in content of quercetin (0.482 % and 0.506 %, respectively, while Pruni spinosae flos and Robiniae pseudoaccaciae flos contained the highest amounts of kaempferol (1.246 % and 0.892 %, respectively. Myricetin was identified and determined only in Betulae folium (0.102 %. The content of total flavonoids in the investigated samples expressed in terms of quercetin ranged from 0.040 to 1.680 %. The proposed HPLC method is convenient for use in routine analysis of myricetin, quercetin and kaempferol, as well as for estimation of total flavonoids content in herbal drugs.

  17. Metabolic profiling of body fluids and multivariate data analysis.

    Science.gov (United States)

    Trezzi, Jean-Pierre; Jäger, Christian; Galozzi, Sara; Barkovits, Katalin; Marcus, Katrin; Mollenhauer, Brit; Hiller, Karsten

    2017-01-01

    Metabolome analyses of body fluids are challenging due pre-analytical variations, such as pre-processing delay and temperature, and constant dynamical changes of biochemical processes within the samples. Therefore, proper sample handling starting from the time of collection up to the analysis is crucial to obtain high quality samples and reproducible results. A metabolomics analysis is divided into 4 main steps: 1) Sample collection, 2) Metabolite extraction, 3) Data acquisition and 4) Data analysis. Here, we describe a protocol for gas chromatography coupled to mass spectrometry (GC-MS) based metabolic analysis for biological matrices, especially body fluids. This protocol can be applied on blood serum/plasma, saliva and cerebrospinal fluid (CSF) samples of humans and other vertebrates. It covers sample collection, sample pre-processing, metabolite extraction, GC-MS measurement and guidelines for the subsequent data analysis. Advantages of this protocol include: •Robust and reproducible metabolomics results, taking into account pre-analytical variations that may occur during the sampling process•Small sample volume required•Rapid and cost-effective processing of biological samples•Logistic regression based determination of biomarker signatures for in-depth data analysis.

  18. An operational modal analysis approach based on parametrically identified multivariable transmissibilities

    Science.gov (United States)

    Devriendt, Christof; De Sitter, Gert; Guillaume, Patrick

    2010-07-01

    In this contribution the approach to identify modal parameters from output-only (scalar) transmissibility measurements [C. Devriendt, P. Guillaume, The use of transmissibility measurements in output-only modal analysis, Mechanical Systems and Signal Processing 21 (7) (2007) 2689-2696] is generalized to multivariable transmissibilities. In general, the poles that are identified from (scalar as well as multivariable) transmissibility measurements do not correspond with the system's poles. However, by combining transmissibility measurements under different loading conditions, it is shown in this paper how model parameters can be identified from multivariable transmissibility measurements.

  19. Multivariate Analysis Techniques for Optimal Vision System Design

    DEFF Research Database (Denmark)

    Sharifzadeh, Sara

    used in this thesis are described. The methodological strategies are outlined including sparse regression and pre-processing based on feature selection and extraction methods, supervised versus unsupervised analysis and linear versus non-linear approaches. One supervised feature selection algorithm......The present thesis considers optimization of the spectral vision systems used for quality inspection of food items. The relationship between food quality, vision based techniques and spectral signature are described. The vision instruments for food analysis as well as datasets of the food items...... (SSPCA) and DCT based characterization of the spectral diffused reflectance images for wavelength selection and discrimination. These methods together with some other state-of-the-art statistical and mathematical analysis techniques are applied on datasets of different food items; meat, diaries, fruits...

  20. Early prediction of wheat quality: analysis during grain development using mass spectrometry and multivariate data analysis

    DEFF Research Database (Denmark)

    Ghirardo, A.; Sørensen, Helle Aagaard; Petersen, M.

    2005-01-01

    Matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry and multivariate data analysis have been used for the determination of wheat quality at different stages of grain development. Wheat varieties with one of two different end-use qualities (i.e. suitable or not suitable...... data analysis, offers a method that can replace the traditional rather time-consuming ones such as gel electrophoresis. This study focused on the determination of wheat quality at 15 dpa, when the grain is due for harvest 1 month later....

  1. Multivariate data analysis of enzyme production for hydrolysis purposes

    DEFF Research Database (Denmark)

    Schmidt, A.S.; Suhr, K.I.

    1999-01-01

    of the structure in the data - possibly combined with analysis of variance (ANOVA). Partial least squares regression (PLSR) showed a clear connection between the two differentdata matrices (the fermentation variables and the hydrolysis variables). Hence, PLSR was suitable for prediction purposes. The hydrolysis...

  2. Multivariate techniques of analysis for ToF-E recoil spectrometry data

    Energy Technology Data Exchange (ETDEWEB)

    Whitlow, H.J.; Bouanani, M.E.; Persson, L.; Hult, M.; Jonsson, P.; Johnston, P.N. [Lund Institute of Technology, Solvegatan, (Sweden), Department of Nuclear Physics; Andersson, M. [Uppsala Univ. (Sweden). Dept. of Organic Chemistry; Ostling, M.; Zaring, C. [Royal institute of Technology, Electrum, Kista, (Sweden), Department of Electronics; Johnston, P.N.; Bubb, I.F.; Walker, B.R.; Stannard, W.B. [Royal Melbourne Inst. of Tech., VIC (Australia); Cohen, D.D.; Dytlewski, N. [Australian Nuclear Science and Technology Organisation, Lucas Heights, NSW (Australia)

    1996-12-31

    Multivariate statistical methods are being developed by the Australian -Swedish Recoil Spectrometry Collaboration for quantitative analysis of the wealth of information in Time of Flight (ToF) and energy dispersive Recoil Spectrometry. An overview is presented of progress made in the use of multivariate techniques for energy calibration, separation of mass-overlapped signals and simulation of ToF-E data. 6 refs., 5 figs.

  3. Using Interactive Graphics to Teach Multivariate Data Analysis to Psychology Students

    Science.gov (United States)

    Valero-Mora, Pedro M.; Ledesma, Ruben D.

    2011-01-01

    This paper discusses the use of interactive graphics to teach multivariate data analysis to Psychology students. Three techniques are explored through separate activities: parallel coordinates/boxplots; principal components/exploratory factor analysis; and cluster analysis. With interactive graphics, students may perform important parts of the…

  4. The Potential of Multivariate Analysis in Assessing Students' Attitude to Curriculum Subjects

    Science.gov (United States)

    Gaotlhobogwe, Michael; Laugharne, Janet; Durance, Isabelle

    2011-01-01

    Background: Understanding student attitudes to curriculum subjects is central to providing evidence-based options to policy makers in education. Purpose: We illustrate how quantitative approaches used in the social sciences and based on multivariate analysis (categorical Principal Components Analysis, Clustering Analysis and General Linear…

  5. Using Interactive Graphics to Teach Multivariate Data Analysis to Psychology Students

    Science.gov (United States)

    Valero-Mora, Pedro M.; Ledesma, Ruben D.

    2011-01-01

    This paper discusses the use of interactive graphics to teach multivariate data analysis to Psychology students. Three techniques are explored through separate activities: parallel coordinates/boxplots; principal components/exploratory factor analysis; and cluster analysis. With interactive graphics, students may perform important parts of the…

  6. Stalked protozoa identification by image analysis and multivariable statistical techniques.

    Science.gov (United States)

    Amaral, A L; Ginoris, Y P; Nicolau, A; Coelho, M A Z; Ferreira, E C

    2008-06-01

    Protozoa are considered good indicators of the treatment quality in activated sludge systems as they are sensitive to physical, chemical and operational processes. Therefore, it is possible to correlate the predominance of certain species or groups and several operational parameters of the plant. This work presents a semiautomatic image analysis procedure for the recognition of the stalked protozoa species most frequently found in wastewater treatment plants by determining the geometrical, morphological and signature data and subsequent processing by discriminant analysis and neural network techniques. Geometrical descriptors were found to be responsible for the best identification ability and the identification of the crucial Opercularia and Vorticella microstoma microorganisms provided some degree of confidence to establish their presence in wastewater treatment plants.

  7. Characterization of Nuclear Fuel using Multivariate Statistical Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Robel, M; Robel, M; Robel, M; Kristo, M J; Kristo, M J

    2007-11-27

    Various combinations of reactor type and fuel composition have been characterized using principle components analysis (PCA) of the concentrations of 9 U and Pu isotopes in the 10 fuel as a function of burnup. The use of PCA allows the reduction of the 9-dimensional data (isotopic concentrations) into a 3-dimensional approximation, giving a visual representation of the changes in nuclear fuel composition with burnup. Real-world variation in the concentrations of {sup 234}U and {sup 236}U in the fresh (unirradiated) fuel was accounted for. The effects of reprocessing were also simulated. The results suggest that, 15 even after reprocessing, Pu isotopes can be used to determine both the type of reactor and the initial fuel composition with good discrimination. Finally, partial least squares discriminant analysis (PSLDA) was investigated as a substitute for PCA. Our results suggest that PLSDA is a better tool for this application where separation between known classes is most important.

  8. BUSINESS FAILURE PREDICTION FOR ROMANIAN SMES USING MULTIVARIATE DISCRIMINANT ANALYSIS

    OpenAIRE

    2012-01-01

    Business failure prediction is one of special importance for small and medium sized enterprises (SMEs) due to their increased vulnerability. Consequently, the purpose of this paper is to investigate the utility of financial ratios and other non-financial variables to predict business failure using a sample of Romanian SMEs and applying multiple discriminant analysis. The process that leads to failure is analyzed on a three year time horizon prior to failure and the results showed that failure...

  9. Stalked protozoa identification by image analysis and multivariable statistical techniques

    OpenAIRE

    Amaral, A.L.; Ginoris, Y. P.; Nicolau, Ana; M.A.Z. Coelho; Ferreira, E. C.

    2008-01-01

    Protozoa are considered good indicators of the treatment quality in activated sludge systems as they are sensitive to physical, chemical and operational processes. Therefore, it is possible to correlate the predominance of certain species or groups and several operational parameters of the plant. This work presents a semiautomatic image analysis procedure for the recognition of the stalked protozoa species most frequently found in wastewater treatment plants by determinin...

  10. Multivariable Discriminant Analysis for the Differential Diagnosis of Microcytic Anemia

    Directory of Open Access Journals (Sweden)

    Eloísa Urrechaga

    2013-01-01

    Full Text Available Introduction. Iron deficiency anemia and thalassemia are the most common causes of microcytic anemia. Powerful statistical computer programming enables sensitive discriminant analyses to aid in the diagnosis. We aimed at investigating the performance of the multiple discriminant analysis (MDA to the differential diagnosis of microcytic anemia. Methods. The training group was composed of 200 β-thalassemia carriers, 65 α-thalassemia carriers, 170 iron deficiency anemia (IDA, and 45 mixed cases of thalassemia and acute phase response or iron deficiency. A set of potential predictor parameters that could detect differences among groups were selected: Red Blood Cells (RBC, hemoglobin (Hb, mean cell volume (MCV, mean cell hemoglobin (MCH, and RBC distribution width (RDW. The functions obtained with MDA analysis were applied to a set of 628 consecutive patients with microcytic anemia. Results. For classifying patients into two groups (genetic anemia and acquired anemia, only one function was needed; 87.9% β-thalassemia carriers, and 83.3% α-thalassemia carriers, and 72.1% in the mixed group were correctly classified. Conclusion. Linear discriminant functions based on hemogram data can aid in differentiating between IDA and thalassemia, so samples can be efficiently selected for further analysis to confirm the presence of genetic anemia.

  11. Quantitative multivariate analysis of dynamic multicellular morphogenic trajectories.

    Science.gov (United States)

    White, Douglas E; Sylvester, Jonathan B; Levario, Thomas J; Lu, Hang; Streelman, J Todd; McDevitt, Todd C; Kemp, Melissa L

    2015-07-01

    Interrogating fundamental cell biology principles that govern tissue morphogenesis is critical to better understanding of developmental biology and engineering novel multicellular systems. Recently, functional micro-tissues derived from pluripotent embryonic stem cell (ESC) aggregates have provided novel platforms for experimental investigation; however elucidating the factors directing emergent spatial phenotypic patterns remains a significant challenge. Computational modelling techniques offer a unique complementary approach to probe mechanisms regulating morphogenic processes and provide a wealth of spatio-temporal data, but quantitative analysis of simulations and comparison to experimental data is extremely difficult. Quantitative descriptions of spatial phenomena across multiple systems and scales would enable unprecedented comparisons of computational simulations with experimental systems, thereby leveraging the inherent power of computational methods to interrogate the mechanisms governing emergent properties of multicellular biology. To address these challenges, we developed a portable pattern recognition pipeline consisting of: the conversion of cellular images into networks, extraction of novel features via network analysis, and generation of morphogenic trajectories. This novel methodology enabled the quantitative description of morphogenic pattern trajectories that could be compared across diverse systems: computational modelling of multicellular structures, differentiation of stem cell aggregates, and gastrulation of cichlid fish. Moreover, this method identified novel spatio-temporal features associated with different stages of embryo gastrulation, and elucidated a complex paracrine mechanism capable of explaining spatiotemporal pattern kinetic differences in ESC aggregates of different sizes.

  12. Bayesian inference on risk differences: an application to multivariate meta-analysis of adverse events in clinical trials.

    Science.gov (United States)

    Chen, Yong; Luo, Sheng; Chu, Haitao; Wei, Peng

    2013-05-01

    Multivariate meta-analysis is useful in combining evidence from independent studies which involve several comparisons among groups based on a single outcome. For binary outcomes, the commonly used statistical models for multivariate meta-analysis are multivariate generalized linear mixed effects models which assume risks, after some transformation, follow a multivariate normal distribution with possible correlations. In this article, we consider an alternative model for multivariate meta-analysis where the risks are modeled by the multivariate beta distribution proposed by Sarmanov (1966). This model have several attractive features compared to the conventional multivariate generalized linear mixed effects models, including simplicity of likelihood function, no need to specify a link function, and has a closed-form expression of distribution functions for study-specific risk differences. We investigate the finite sample performance of this model by simulation studies and illustrate its use with an application to multivariate meta-analysis of adverse events of tricyclic antidepressants treatment in clinical trials.

  13. PRINCIPAL COMPONENT ANALYSIS AND CLUSTER ANALYSIS IN MULTIVARIATE ASSESSMENT OF WATER QUALITY

    Directory of Open Access Journals (Sweden)

    Elzbieta Radzka

    2017-03-01

    Full Text Available This paper deals with the use of multivariate methods in drinking water analysis. During a five-year project, from 2008 to 2012, selected chemical parameters in 11 water supply networks of the Siedlce County were studied. Throughout that period drinking water was of satisfactory quality, with only iron and manganese ions exceeding the limits (21 times and 12 times, respectively. In accordance with the results of cluster analysis, all water networks were put into three groups of different water quality. A high concentration of chlorides, sulphates, and manganese and a low concentration of copper and sodium was found in the water of Group 1 supply networks. The water in Group 2 had a high concentration of copper and sodium, and a low concentration of iron and sulphates. The water from Group 3 had a low concentration of chlorides and manganese, but a high concentration of fluorides. Using principal component analysis and cluster analysis, multivariate correlation between the studied parameters was determined, helping to put water supply networks into groups according to similar water quality.

  14. Discrimination of aqueous and vinegary extracts of Shixiao San using metabolomics coupled with multivariate data analysis and evaluation of anti-hyperlipidemic effect

    Directory of Open Access Journals (Sweden)

    Xiaofan Wang

    2014-02-01

    Full Text Available A novel study using LC–MS (Liquid chromatography tandem mass spectrometry coupled with multivariate data analysis and bioactivity evaluation was established for discrimination of aqueous extract and vinegar extract of Shixiao San. Batches of these two kinds of samples were subjected to analysis, and the datasets of sample codes, tR-m/z pairs and ion intensities were processed with principal component analysis (PCA. The result of score plot showed a clear classification of the aqueous and vinegar groups. And the chemical markers having great contributions to the differentiation were screened out on the loading plot. The identities of the chemical markers were performed by comparing the mass fragments and retention times with those of reference compounds and/or the known compounds published in the literatures. Based on the proposed strategy, quercetin-3-O-neohesperidoside, isorhamnetin-3-O-neohespeeridoside, kaempferol-3-O-neohesperidoside, isorhamnetin-3-O-rutinoside and isorhamnetin-3-O-(2G-α-l-rhamnosyl-rutinoside were explored as representative markers in distinguishing the vinegar extract from the aqueous extract. The anti-hyperlipidemic activities of two processed extracts of Shixiao San were examined on serum levels of lipids, lipoprotein and blood antioxidant enzymes in a rat hyperlipidemia model, and the vinegary extract, exerting strong lipid-lowering and antioxidative effects, was superior to the aqueous extract. Therefore, boiling with vinegary was predicted as the greatest processing procedure for anti-hyperlipidemic effect of Shixiao San. Furthermore, combining the changes in the metabolic profiling and bioactivity evaluation, the five representative markers may be related to the observed anti-hyperlipidemic effect.

  15. Machine processing for remotely acquired data. [using multivariate statistical analysis

    Science.gov (United States)

    Landgrebe, D. A.

    1974-01-01

    This paper is a general discussion of earth resources information systems which utilize airborne and spaceborne sensors. It points out that information may be derived by sensing and analyzing the spectral, spatial and temporal variations of electromagnetic fields emanating from the earth surface. After giving an overview system organization, the two broad categories of system types are discussed. These are systems in which high quality imagery is essential and those more numerically oriented. Sensors are also discussed with this categorization of systems in mind. The multispectral approach and pattern recognition are described as an example data analysis procedure for numerically-oriented systems. The steps necessary in using a pattern recognition scheme are described and illustrated with data obtained from aircraft and the Earth Resources Technology Satellite (ERTS-1).

  16. Random matrix approach to multivariate categorical data analysis

    CERN Document Server

    Patil, Aashay

    2015-01-01

    Correlation and similarity measures are widely used in all the areas of sciences and social sciences. Often the variables are not numbers but are instead qualitative descriptors called categorical data. We define and study similarity matrix, as a measure of similarity, for the case of categorical data. This is of interest due to a deluge of categorical data, such as movie ratings, top-10 rankings and data from social media, in the public domain that require analysis. We show that the statistical properties of the spectra of similarity matrices, constructed from categorical data, follow those from random matrix theory. We demonstrate this approach by applying it to the data of Indian general elections and sea level pressures in North Atlantic ocean.

  17. Multivariate Data Analysis for Neuroimaging Data: Overview and Application to Alzheimer’s Disease

    Science.gov (United States)

    Stern, Yaakov

    2010-01-01

    As clinical and cognitive neuroscience mature, the need for sophisticated neuroimaging analysis becomes more apparent. Multivariate analysis techniques have recently received increasing attention as they have many attractive features that cannot be easily realized by the more commonly used univariate, voxel-wise, techniques. Multivariate approaches evaluate correlation/covariance of activation across brain regions, rather than proceeding on a voxel-by-voxel basis. Thus, their results can be more easily interpreted as a signature of neural networks. Univariate approaches, on the other hand, cannot directly address functional connectivity in the brain. The covariance approach can also result in greater statistical power when compared with univariate techniques, which are forced to employ very stringent, and often overly conservative, corrections for voxel-wise multiple comparisons. Multivariate techniques also lend themselves much better to prospective application of results from the analysis of one dataset to entirely new datasets. Multivariate techniques are thus well placed to provide information about mean differences and correlations with behavior, similarly to univariate approaches, with potentially greater statistical power and better reproducibility checks. In contrast to these advantages is the high barrier of entry to the use of multivariate approaches, preventing more widespread application in the community. To the neuroscientist becoming familiar with multivariate analysis techniques, an initial survey of the field might present a bewildering variety of approaches that, although algorithmically similar, are presented with different emphases, typically by people with mathematics backgrounds. We believe that multivariate analysis techniques have sufficient potential to warrant better dissemination. Researchers should be able to employ them in an informed and accessible manner. The following article attempts to provide a basic introduction with sample

  18. A non-iterative extension of the multivariate random effects meta-analysis.

    Science.gov (United States)

    Makambi, Kepher H; Seung, Hyunuk

    2015-01-01

    Multivariate methods in meta-analysis are becoming popular and more accepted in biomedical research despite computational issues in some of the techniques. A number of approaches, both iterative and non-iterative, have been proposed including the multivariate DerSimonian and Laird method by Jackson et al. (2010), which is non-iterative. In this study, we propose an extension of the method by Hartung and Makambi (2002) and Makambi (2001) to multivariate situations. A comparison of the bias and mean square error from a simulation study indicates that, in some circumstances, the proposed approach perform better than the multivariate DerSimonian-Laird approach. An example is presented to demonstrate the application of the proposed approach.

  19. Multivariate analysis of complex gene expression and clinical phenotypes with genetic marker data.

    Science.gov (United States)

    Beyene, Joseph; Tritchler, David; Bull, Shelley B; Cartier, Kevin C; Jonasdottir, Gudrun; Kraja, Aldi T; Li, Na; Nock, Nora L; Parkhomenko, Elena; Rao, J Sunil; Stein, Catherine M; Sutradhar, Rinku; Waaijenborg, Sandra; Wang, Ke-Sheng; Wang, Yuanjia; Wolkow, Pavel

    2007-01-01

    This paper summarizes contributions to group 12 of the 15th Genetic Analysis Workshop. The papers in this group focused on multivariate methods and applications for the analysis of molecular data including genotypic data as well as gene expression microarray measurements and clinical phenotypes. A range of multivariate techniques have been employed to extract signals from the multi-feature data sets that were provided by the workshop organizers. The methods included data reduction techniques such as principal component analysis and cluster analysis; latent variable models including structural equations and item response modeling; joint multivariate modeling techniques as well as multivariate visualization tools. This summary paper categorizes and discusses individual contributions with regard to multiple classifications of multivariate methods. Given the wide variety in the data considered, the objectives of the analysis and the methods applied, direct comparison of the results of the various papers is difficult. However, the group was able to make many interesting comparisons and parallels between the various approaches. In summary, there was a consensus among authors in group 12 that the genetic research community should continue to draw experiences from other fields such as statistics, econometrics, chemometrics, computer science and linear systems theory.

  20. Multivariate Autoregressive Modeling and Granger Causality Analysis of Multiple Spike Trains

    Directory of Open Access Journals (Sweden)

    Michael Krumin

    2010-01-01

    Full Text Available Recent years have seen the emergence of microelectrode arrays and optical methods allowing simultaneous recording of spiking activity from populations of neurons in various parts of the nervous system. The analysis of multiple neural spike train data could benefit significantly from existing methods for multivariate time-series analysis which have proven to be very powerful in the modeling and analysis of continuous neural signals like EEG signals. However, those methods have not generally been well adapted to point processes. Here, we use our recent results on correlation distortions in multivariate Linear-Nonlinear-Poisson spiking neuron models to derive generalized Yule-Walker-type equations for fitting ‘‘hidden’’ Multivariate Autoregressive models. We use this new framework to perform Granger causality analysis in order to extract the directed information flow pattern in networks of simulated spiking neurons. We discuss the relative merits and limitations of the new method.

  1. A refined method for multivariate meta-analysis and meta-regression.

    Science.gov (United States)

    Jackson, Daniel; Riley, Richard D

    2014-02-20

    Making inferences about the average treatment effect using the random effects model for meta-analysis is problematic in the common situation where there is a small number of studies. This is because estimates of the between-study variance are not precise enough to accurately apply the conventional methods for testing and deriving a confidence interval for the average effect. We have found that a refined method for univariate meta-analysis, which applies a scaling factor to the estimated effects' standard error, provides more accurate inference. We explain how to extend this method to the multivariate scenario and show that our proposal for refined multivariate meta-analysis and meta-regression can provide more accurate inferences than the more conventional approach. We explain how our proposed approach can be implemented using standard output from multivariate meta-analysis software packages and apply our methodology to two real examples. Copyright © 2013 John Wiley & Sons, Ltd.

  2. Multivariate analysis of remote LIBS spectra using partial least squares, principal component analysis, and related techniques

    Energy Technology Data Exchange (ETDEWEB)

    Clegg, Samuel M [Los Alamos National Laboratory; Barefield, James E [Los Alamos National Laboratory; Wiens, Roger C [Los Alamos National Laboratory; Sklute, Elizabeth [MT HOLYOKE COLLEGE; Dyare, Melinda D [MT HOLYOKE COLLEGE

    2008-01-01

    Quantitative analysis with LIBS traditionally employs calibration curves that are complicated by the chemical matrix effects. These chemical matrix effects influence the LIBS plasma and the ratio of elemental composition to elemental emission line intensity. Consequently, LIBS calibration typically requires a priori knowledge of the unknown, in order for a series of calibration standards similar to the unknown to be employed. In this paper, three new Multivariate Analysis (MV A) techniques are employed to analyze the LIBS spectra of 18 disparate igneous and highly-metamorphosed rock samples. Partial Least Squares (PLS) analysis is used to generate a calibration model from which unknown samples can be analyzed. Principal Components Analysis (PCA) and Soft Independent Modeling of Class Analogy (SIMCA) are employed to generate a model and predict the rock type of the samples. These MV A techniques appear to exploit the matrix effects associated with the chemistries of these 18 samples.

  3. Multivariate analysis relating oil shale geochemical properties to NMR relaxometry

    Science.gov (United States)

    Birdwell, Justin E.; Washburn, Kathryn E.

    2015-01-01

    Low-field nuclear magnetic resonance (NMR) relaxometry has been used to provide insight into shale composition by separating relaxation responses from the various hydrogen-bearing phases present in shales in a noninvasive way. Previous low-field NMR work using solid-echo methods provided qualitative information on organic constituents associated with raw and pyrolyzed oil shale samples, but uncertainty in the interpretation of longitudinal-transverse (T1–T2) relaxometry correlation results indicated further study was required. Qualitative confirmation of peaks attributed to kerogen in oil shale was achieved by comparing T1–T2 correlation measurements made on oil shale samples to measurements made on kerogen isolated from those shales. Quantitative relationships between T1–T2 correlation data and organic geochemical properties of raw and pyrolyzed oil shales were determined using partial least-squares regression (PLSR). Relaxometry results were also compared to infrared spectra, and the results not only provided further confidence in the organic matter peak interpretations but also confirmed attribution of T1–T2 peaks to clay hydroxyls. In addition, PLSR analysis was applied to correlate relaxometry data to trace element concentrations with good success. The results of this work show that NMR relaxometry measurements using the solid-echo approach produce T1–T2 peak distributions that correlate well with geochemical properties of raw and pyrolyzed oil shales.

  4. A multivariate analysis of serum nutrient levels and lung function

    Directory of Open Access Journals (Sweden)

    Smit Henriette A

    2008-09-01

    Full Text Available Abstract Background There is mounting evidence that estimates of intakes of a range of dietary nutrients are related to both lung function level and rate of decline, but far less evidence on the relation between lung function and objective measures of serum levels of individual nutrients. The aim of this study was to conduct a comprehensive examination of the independent associations of a wide range of serum markers of nutritional status with lung function, measured as the one-second forced expiratory volume (FEV1. Methods Using data from the Third National Health and Nutrition Examination Survey, a US population-based cross-sectional study, we investigated the relation between 21 serum markers of potentially relevant nutrients and FEV1, with adjustment for potential confounding factors. Systematic approaches were used to guide the analysis. Results In a mutually adjusted model, higher serum levels of antioxidant vitamins (vitamin A, beta-cryptoxanthin, vitamin C, vitamin E, selenium, normalized calcium, chloride, and iron were independently associated with higher levels of FEV1. Higher concentrations of potassium and sodium were associated with lower FEV1. Conclusion Maintaining higher serum concentrations of dietary antioxidant vitamins and selenium is potentially beneficial to lung health. In addition other novel associations found in this study merit further investigation.

  5. A comparison between multivariate and bivariate analysis used in marketing research

    Directory of Open Access Journals (Sweden)

    Constantin, C.

    2012-01-01

    Full Text Available This paper is about an instrumental research conducted in order to compare the information given by two multivariate data analysis in comparison with the usual bivariate analysis. The outcomes of the research reveal that sometimes the multivariate methods use more information from a certain variable, but sometimes they use only a part of the information considered the most important for certain associations. For this reason, a researcher should use both categories of data analysis in order to obtain entirely useful information.

  6. Multivariate Analysis, Retrieval, and Storage System (MARS). Volume 1: MARS System and Analysis Techniques

    Science.gov (United States)

    Hague, D. S.; Vanderberg, J. D.; Woodbury, N. W.

    1974-01-01

    A method for rapidly examining the probable applicability of weight estimating formulae to a specific aerospace vehicle design is presented. The Multivariate Analysis Retrieval and Storage System (MARS) is comprised of three computer programs which sequentially operate on the weight and geometry characteristics of past aerospace vehicles designs. Weight and geometric characteristics are stored in a set of data bases which are fully computerized. Additional data bases are readily added to the MARS system and/or the existing data bases may be easily expanded to include additional vehicles or vehicle characteristics.

  7. Integrated Analysis of Tropical Trees Growth: A Multivariate Approach

    Science.gov (United States)

    YÁÑEZ-ESPINOSA, LAURA; TERRAZAS, TERESA; LÓPEZ-MATA, LAURO

    2006-01-01

    • Background and Aims One of the problems analysing cause–effect relationships of growth and environmental factors is that a single factor could be correlated with other ones directly influencing growth. One attempt to understand tropical trees' growth cause–effect relationships is integrating research about anatomical, physiological and environmental factors that influence growth in order to develop mathematical models. The relevance is to understand the nature of the process of growth and to model this as a function of the environment. • Methods The relationships of Aphananthe monoica, Pleuranthodendron lindenii and Psychotria costivenia radial growth and phenology with environmental factors (local climate, vertical strata microclimate and physical and chemical soil variables) were evaluated from April 2000 to September 2001. The association among these groups of variables was determined by generalized canonical correlation analysis (GCCA), which considers the probable associations of three or more data groups and the selection of the most important variables for each data group. • Key Results The GCCA allowed determination of a general model of relationships among tree phenology and radial growth with climate, microclimate and soil factors. A strong influence of climate in phenology and radial growth existed. Leaf initiation and cambial activity periods were associated with maximum temperature and day length, and vascular tissue differentiation with soil moisture and rainfall. The analyses of individual species detected different relationships for the three species. • Conclusions The analyses of the individual species suggest that each one takes advantage in a different way of the environment in which they are growing, allowing them to coexist. PMID:16822807

  8. A Multivariate Analysis of Freshwater Variability over West Africa

    Science.gov (United States)

    Andam-Akorful, S. A.; He, X.; Ferreira, V. G.; Quaye-Ballard, J. A.

    2015-12-01

    As one of the most vulnerable regions to climate change, West Africa (WA) has since the 1970s suffered sustained reduction in rainfall amounts, leading to droughts and associated negative impacts on its water resources. Although rainfall rates have been reported to have experienced a degree of recovery, dry conditions persist. Additionally, the region faces perennial flooding, thus resulting in a highly variable hydrologic regime due to the extreme climate conditions. This therefore necessitates routine monitoring of the WA's freshwater reserves and its response to climate variations at the short and long term scales to aid sustainable use and management. However, this monitoring is hampered by data deficiency issues within the region. Consequently, dynamics leading to changes in water availability over the region are not completely understood. In this work, the recent flux and state of freshwater availability over WA from 1979 to 2013 is assessed by investigating the coupled variability of GRACE-derived terrestrial water storage (TWS) and its changes (TWSC) estimates with rainfall, evapotranspiration, and land surface air temperature (LSAT), as well as, major global and regional teleconnection indices using complex principal component analysis and wavelet transforms. Since GRACE covers a relatively short period, and thereby present challenges for long to medium term analyses, Artificial Neural Network (ANN) is employed to extend the GRACE series to 1979. The results from the ANN proved to be robust upon evaluation; spatially-averaged series for major basins and sub-climatic zones, as well as, the whole of WA presented RMSE, Nash-Sutcliffe efficient, and coefficient of determination (R2) of 11.83 mm, 0.76 and 0.89 respectively. Overall, the results obtained from this study indicate that, sustained increase in water flux, in terms of TWSC, contributed to a resurgence in freshwater reserves in the 21st century over WA from the low levels in the late 20th century

  9. MUMAL: Multivariate analysis in shotgun proteomics using machine learning techniques

    Directory of Open Access Journals (Sweden)

    Cerqueira Fabio R

    2012-10-01

    Full Text Available Abstract Background The shotgun strategy (liquid chromatography coupled with tandem mass spectrometry is widely applied for identification of proteins in complex mixtures. This method gives rise to thousands of spectra in a single run, which are interpreted by computational tools. Such tools normally use a protein database from which peptide sequences are extracted for matching with experimentally derived mass spectral data. After the database search, the correctness of obtained peptide-spectrum matches (PSMs needs to be evaluated also by algorithms, as a manual curation of these huge datasets would be impractical. The target-decoy database strategy is largely used to perform spectrum evaluation. Nonetheless, this method has been applied without considering sensitivity, i.e., only error estimation is taken into account. A recently proposed method termed MUDE treats the target-decoy analysis as an optimization problem, where sensitivity is maximized. This method demonstrates a significant increase in the retrieved number of PSMs for a fixed error rate. However, the MUDE model is constructed in such a way that linear decision boundaries are established to separate correct from incorrect PSMs. Besides, the described heuristic for solving the optimization problem has to be executed many times to achieve a significant augmentation in sensitivity. Results Here, we propose a new method, termed MUMAL, for PSM assessment that is based on machine learning techniques. Our method can establish nonlinear decision boundaries, leading to a higher chance to retrieve more true positives. Furthermore, we need few iterations to achieve high sensitivities, strikingly shortening the running time of the whole process. Experiments show that our method achieves a considerably higher number of PSMs compared with standard tools such as MUDE, PeptideProphet, and typical target-decoy approaches. Conclusion Our approach not only enhances the computational performance, and

  10. Volatility Spillover and Multivariate Volatility Impulse Response Analysis of GFC News Events

    NARCIS (Netherlands)

    D.E. Allen (David); M.J. McAleer (Michael); R.J. Powell (Robert); A.K. Singh (Abhay)

    2016-01-01

    textabstractThis paper applies two measures to assess spillovers across markets: the Diebold Yilmaz (2012) Spillover Index and the Hafner and Herwartz (2006) analysis of multivariate GARCH models using volatility impulse response analysis. We use two sets of data, daily realized volatility estimates

  11. Exploratory Analysis of Multivariate Data (Unsupervised Image Segmentation and Data Driven Linear and Nonlinear Decomposition)

    DEFF Research Database (Denmark)

    Hilger, Klaus Baggesen

    2002-01-01

    This work describes different methods that are useful in the analysis of multivariate single and multiset data. The thesis covers selected aspects of relevant data analysis techniques in this context. Methods dedicated to handling data of a spatial nature are of primary interest with focus on dat...

  12. Multivariate data analysis as a PAT tool for early bioprocess development data

    NARCIS (Netherlands)

    Mercier, S.M.; Diepenbroek, B.; Dalm, M.C.F.; Wijffels, R.H.; Streefland, M.

    2013-01-01

    Early development datasets are typically unstructured, incomplete and truncated, yet they are readily available and contain relevant process information which is not extracted using classical data analysis techniques. In this paper, we illustrate the power of multivariate data analysis (MVDA) as a

  13. Intraoperative imaging of cortical cerebral perfusion by time-resolved thermography and multivariate data analysis

    Science.gov (United States)

    Steiner, Gerald; Sobottka, Stephan B.; Koch, Edmund; Schackert, Gabriele; Kirsch, Matthias

    2011-01-01

    A new approach to cortical perfusion imaging is demonstrated using high-sensitivity thermography in conjunction with multivariate statistical data analysis. Local temperature changes caused by a cold bolus are imaged and transferred to a false color image. A cold bolus of 10 ml saline at ice temperature is injected systemically via a central venous access. During the injection, a sequence of 735 thermographic images are recorded within 2 min. The recorded data cube is subjected to a principal component analysis (PCA) to select slight changes of the cortical temperature caused by the cold bolus. PCA reveals that 11 s after injection the temperature of blood vessels is shortly decreased followed by an increase to the temperature before the cold bolus is injected. We demonstrate the potential of intraoperative thermography in combination with multivariate data analysis to image cortical cerebral perfusion without any markers. We provide the first in vivo application of multivariate thermographic imaging.

  14. The application of multivariate data analysis in the interpretation of engineering geological parameters

    Science.gov (United States)

    Kovács, József; Bodnár, Nikolett; Török, Ákos

    2016-01-01

    The paper presents the evaluation of engineering geological laboratory test results of core drillings along the new metro line (line 4) in Budapest by using a multivariate data analysis. A data set of 30 core drillings with a total coring length of over 1500 meters was studied. Of the eleven engineering geological parameters considered in this study, only the five most reliable (void ratio, dry bulk density, angle of internal friction, cohesion and compressive strength) representing 1260 data points were used for multivariate (cluster and discriminant) analyses. To test the results of the cluster analysis discriminant analysis was used. The results suggest that the use of multivariate analyses allows the identification of different groups of sediments even when the data sets are overlapping and contain several uncertainties. The tests also prove that the use of these methods for seemingly very scattered parameters is crucial in obtaining reliable engineering geological data for design.

  15. In vitro anti-HIV-1 activities of kaempferol and kaempferol-7-O-glucoside isolated from Securigera securidaca.

    Science.gov (United States)

    Behbahani, M; Sayedipour, S; Pourazar, A; Shanehsazzadeh, M

    2014-01-01

    Previously, we reported that the kaempferol and kaempferol-7-O-glucoside isolated from Securigera securidaca showed potent anti-HSV activity. In the present study the anti-HIV-1 activities of kaempferol and kaempferol-7-O-glucoside are investigated at different concentrations (100, 50, 25 and 10 μg/ml) using HIV-1 p24 Antigen kit. Real-time Polymerase chain reaction (RT-PCR) assay was also used for quantification of full range of virus load observed in treated and untreated cells. According to the results of RT- PCR, tested compounds at a concentration of 100 μg/ml exerted potent inhibitory effect. Time of drug addition experiments demonstrated that these compounds exerted their inhibitory effects on the early stage of HIV infection. The results also showed potent anti-HIV-1 reverse transcriptase activity. Antiviral activity of kaempferol-7-O-glucoside was more pronounced than that of kaempferol. These findings demonstrate that kaempferol-7-O-glucoside could be considered as a new potential drug candidate for the treatment of HIV infection which requires further assessments.

  16. Analysis of Maize Crop Leaf using Multivariate Image Analysis for Identifying Soil Deficiency

    Directory of Open Access Journals (Sweden)

    S. Sridevy

    2014-11-01

    Full Text Available Image processing analysis for the soil deficiency identification has become an active area of research in this study. The changes in the color of the leaves are used to analyze and identify the deficiency of soil nutrients such as Nitrogen (N, Phosphorus (P and potassium (K by digital color image analysis. This research study focuses on the image analysis of the maize crop leaf using multivariate image analysis. In this proposed novel approach, initially, a color transformation for the input RGB image is formed and this RGB is converted to HSV because RGB is ideal for color generation but HSV is very suitable for color perception. Then green pixels are masked and removed using specific threshold value by applying histogram equalization. This masking approach is done through specific customized filtering approach which exclusively filters the green color of the leaf. After the filtering step, only the deficiency part of the leaf is taken for consideration. Then, a histogram generation is carried out for the deficiency part of the leaf. Then, Multivariate Image Analysis approach using Independent Component Analysis (ICA is carried out to extract a reference eigenspace from a matrix built by unfolding color data from the deficiency part. Test images are also unfolded and projected onto the reference eigenspace and the result is a score matrix which is used to compute nutrient deficiency based on the T2 statistic. In addition, a multi-resolution scheme by scaling down process is carried out to speed up the process. Finally, based on the training samples, the soil deficiency is identified based on the color of the maize crop leaf.

  17. Chemical Discrimination of Cortex Phellodendri amurensis and Cortex Phellodendri chinensis by Multivariate Analysis Approach.

    Science.gov (United States)

    Sun, Hui; Wang, Huiyu; Zhang, Aihua; Yan, Guangli; Han, Ying; Li, Yuan; Wu, Xiuhong; Meng, Xiangcai; Wang, Xijun

    2016-01-01

    As herbal medicines have an important position in health care systems worldwide, their current assessment, and quality control are a major bottleneck. Cortex Phellodendri chinensis (CPC) and Cortex Phellodendri amurensis (CPA) are widely used in China, however, how to identify species of CPA and CPC has become urgent. In this study, multivariate analysis approach was performed to the investigation of chemical discrimination of CPA and CPC. Principal component analysis showed that two herbs could be separated clearly. The chemical markers such as berberine, palmatine, phellodendrine, magnoflorine, obacunone, and obaculactone were identified through the orthogonal partial least squared discriminant analysis, and were identified tentatively by the accurate mass of quadruple-time-of-flight mass spectrometry. A total of 29 components can be used as the chemical markers for discrimination of CPA and CPC. Of them, phellodenrine is significantly higher in CPC than that of CPA, whereas obacunone and obaculactone are significantly higher in CPA than that of CPC. The present study proves that multivariate analysis approach based chemical analysis greatly contributes to the investigation of CPA and CPC, and showed that the identified chemical markers as a whole should be used to discriminate the two herbal medicines, and simultaneously the results also provided chemical information for their quality assessment. Multivariate analysis approach was performed to the investigate the herbal medicineThe chemical markers were identified through multivariate analysis approachA total of 29 components can be used as the chemical markers. UPLC-Q/TOF-MS-based multivariate analysis method for the herbal medicine samples Abbreviations used: CPC: Cortex Phellodendri chinensis, CPA: Cortex Phellodendri amurensis, PCA: Principal component analysis, OPLS-DA: Orthogonal partial least squares discriminant analysis, BPI: Base peaks ion intensity.

  18. Power analysis for multivariate and repeated measures designs: a flexible approach using the SPSS MANOVA procedure.

    Science.gov (United States)

    D'Amico, E J; Neilands, T B; Zambarano, R

    2001-11-01

    Although power analysis is an important component in the planning and implementation of research designs, it is often ignored. Computer programs for performing power analysis are available, but most have limitations, particularly for complex multivariate designs. An SPSS procedure is presented that can be used for calculating power for univariate, multivariate, and repeated measures models with and without time-varying and time-constant covariates. Three examples provide a framework for calculating power via this method: an ANCOVA, a MANOVA, and a repeated measures ANOVA with two or more groups. The benefits and limitations of this procedure are discussed.

  19. Multivariate statistical analysis: Principles and applications to coorbital streams of meteorite falls

    Science.gov (United States)

    Wolf, S. F.; Lipschutz, M. E.

    1993-01-01

    Multivariate statistical analysis techniques (linear discriminant analysis and logistic regression) can provide powerful discrimination tools which are generally unfamiliar to the planetary science community. Fall parameters were used to identify a group of 17 H chondrites (Cluster 1) that were part of a coorbital stream which intersected Earth's orbit in May, from 1855 - 1895, and can be distinguished from all other H chondrite falls. Using multivariate statistical techniques, it was demonstrated that a totally different criterion, labile trace element contents - hence thermal histories - or 13 Cluster 1 meteorites are distinguishable from those of 45 non-Cluster 1 H chondrites. Here, we focus upon the principles of multivariate statistical techniques and illustrate their application using non-meteoritic and meteoritic examples.

  20. Ripening of salami: assessment of colour and aspect evolution using image analysis and multivariate image analysis.

    Science.gov (United States)

    Fongaro, Lorenzo; Alamprese, Cristina; Casiraghi, Ernestina

    2015-03-01

    During ripening of salami, colour changes occur due to oxidation phenomena involving myoglobin. Moreover, shrinkage due to dehydration results in aspect modifications, mainly ascribable to fat aggregation. The aim of this work was the application of image analysis (IA) and multivariate image analysis (MIA) techniques to the study of colour and aspect changes occurring in salami during ripening. IA results showed that red, green, blue, and intensity parameters decreased due to the development of a global darker colour, while Heterogeneity increased due to fat aggregation. By applying MIA, different salami slice areas corresponding to fat and three different degrees of oxidised meat were identified and quantified. It was thus possible to study the trend of these different areas as a function of ripening, making objective an evaluation usually performed by subjective visual inspection. Copyright © 2014 Elsevier Ltd. All rights reserved.

  1. Anti-inflammatory effects of kaempferol, myricetin, fisetin and ...

    African Journals Online (AJOL)

    Tropical Journal of Pharmaceutical Research August 2017; 16 (8): 1819-1826 ... regulation [6-8]. The objective of this study was to investigate the ... plant compounds kaempferol, myricetin and .... RMSD threshold for multiple cluster poses was.

  2. Multivariate analysis of TOF-SIMS spectra from self-assembled monolayers

    Science.gov (United States)

    Graham, Daniel Jay

    Recently the concept of engineered biomaterial surfaces has started a revolution in the biomaterials community. These biomaterial surfaces are designed using knowledge from cell biology to produce a healing response that will integrate the biomaterials into the body. These surfaces will require specific, complex chemistries that will elicit the desired responses. Such complex surfaces will require an equally detailed surface characterization method. Due to its molecular specificity and high sensitivity, TOF-SIMS appears to be an ideal method for this challenge. Nevertheless TOF-SIMS spectra are complex and difficult to interpret. This complexity results from the shear number of peaks within the spectra, the inter-related nature of the peaks, and lack of fundamental understanding of TOF-SIMS fragmentation mechanisms. This work approaches addressing these problems through use of multivariate analysis. Multivariate analysis enables detailed spectral interpretation and provides insight into fragmentation mechanisms by extracting the salient information from within the complex spectral data set. Multivariate spectral interpretation was explored using a series of self-assembled monolayers that varied in surface order, surface functionality, formation method, and chain length. A multivariate SAM ratio was developed that correlates with thermodynamic properties of the surface. This ratio is the first to demonstrate a direct relationship between TOF-SIMS data and surface thermodynamic parameters. A model for TOF-SIMS fragmentation of SAMs was created and explored using multivariate analysis of a thiol containing a hydroxyl end group. This model explains the emission of fragments from the surface over a time course experiment. This is the first use of multivariate analysis with TOF-SIMS data to provide mechanistic information about the TOF-SIMS process. This methodology provides a technique for studying TOF-SIMS fragmentation using actual data without the need for molecular

  3. A review of the dietary flavonoid, kaempferol on human health and cancer chemoprevention

    OpenAIRE

    Chen, Allen Y.; Chen, Yi Charlie

    2012-01-01

    Kaempferol is a polyphenol antioxidant found in fruits and vegetables. Many studies have described the beneficial effects of dietary kaempferol in reducing the risk of chronic diseases, especially cancer. Epidemiological studies have shown an inverse relationship between kaempferol intake and cancer. Kaempferol may help by augmenting the body’s antioxidant defense against free radicals, which promote the development of cancer. At the molecular level, kaempferol has been reported to modulate a...

  4. Hepatoprotective effect of kaempferol against alcoholic liver injury in mice.

    Science.gov (United States)

    Wang, Meng; Sun, Jianguo; Jiang, Zhihui; Xie, Wenyan; Zhang, Xiaoying

    2015-01-01

    Kaempferol is a biologically active component present in various plants. The hepatoprotective effect of kaempferol in drug-induced liver injury has been proven, while its effect against alcoholic liver injury (ALI) remains unclear. Hence, the present study aimed to evaluate the effect of kaempferol against ALI in mice. The experimental ALI mice model was developed and the mice were treated with different doses of kaempferol for 4 weeks. The liver functions were observed by monitoring the following parameters: Aspartate aminotransferase (AST/GOT) and alanine aminotransferase (ALT/GPT) levels in serum; histopathological studies of liver tissue; oxidative stress by hydrogen peroxide (H2O2), superoxide dismutase (SOD) and glutathione (GSH); the lipid peroxidation status by malondialdehyde (MDA) and lipid accumulation by triglyceride (TG) level in serum; and the expression levels and activities of a key microsomal enzyme cytochrome 2E1 (CYP2E1), by both in vitro and in vivo methods. The ALI mice (untreated) showed clear symptoms of liver injury, such as significantly increased levels of oxidative stress, lipid peroxidation and excessive CYP2E1 expression and activity. The mice treated with different kaempferol dosages exhibited a significant decrease in the oxidative stress as well as lipid peroxidation, and increased anti-oxidative defense activity. The kaempferol treatment has significantly reduced the expression level and activity of hepatic CYP2E1, thus indicating that kaempferol could down regulate CYP2E1. These findings show the hepatoprotective properties of kaempferol against alcohol-induced liver injury by attenuating the activity and expression of CYP2E1 and by enhancing the protective role of anti-oxidative defense system.

  5. Multivariate analysis of progressive thermal desorption coupled gas chromatography-mass spectrometry.

    Energy Technology Data Exchange (ETDEWEB)

    Van Benthem, Mark Hilary; Mowry, Curtis Dale; Kotula, Paul Gabriel; Borek, Theodore Thaddeus, III

    2010-09-01

    Thermal decomposition of poly dimethyl siloxane compounds, Sylgard{reg_sign} 184 and 186, were examined using thermal desorption coupled gas chromatography-mass spectrometry (TD/GC-MS) and multivariate analysis. This work describes a method of producing multiway data using a stepped thermal desorption. The technique involves sequentially heating a sample of the material of interest with subsequent analysis in a commercial GC/MS system. The decomposition chromatograms were analyzed using multivariate analysis tools including principal component analysis (PCA), factor rotation employing the varimax criterion, and multivariate curve resolution. The results of the analysis show seven components related to offgassing of various fractions of siloxanes that vary as a function of temperature. Thermal desorption coupled with gas chromatography-mass spectrometry (TD/GC-MS) is a powerful analytical technique for analyzing chemical mixtures. It has great potential in numerous analytic areas including materials analysis, sports medicine, in the detection of designer drugs; and biological research for metabolomics. Data analysis is complicated, far from automated and can result in high false positive or false negative rates. We have demonstrated a step-wise TD/GC-MS technique that removes more volatile compounds from a sample before extracting the less volatile compounds. This creates an additional dimension of separation before the GC column, while simultaneously generating three-way data. Sandia's proven multivariate analysis methods, when applied to these data, have several advantages over current commercial options. It also has demonstrated potential for success in finding and enabling identification of trace compounds. Several challenges remain, however, including understanding the sources of noise in the data, outlier detection, improving the data pretreatment and analysis methods, developing a software tool for ease of use by the chemist, and demonstrating our belief

  6. On the use of Biplot analysis for multivariate bibliometric and scientific indicators

    OpenAIRE

    Torres-Salinas, Daniel; Robinson-Garc??a, Nicol??s; Jim??nez-Contreras, Evaristo; Herrera, Francisco; Delgado L??pez-C??zar, Emilio

    2013-01-01

    Bibliometric mapping and visualization techniques represent one of the main pillars in the field of scientometrics. Traditionally, the main methodologies employed for representing data are Multi-Dimensional Scaling, Principal Component Analysis or Correspondence Analysis. In this paper we aim at presenting a visualization methodology known as Biplot analysis for representing bibliometric and science and technology indicators. A Biplot is a graphical representation of multivariate data, where ...

  7. Study of the interaction of kaempferol with bovine serum albumin

    Science.gov (United States)

    Tian, Jianniao; Liu, Jiaqin; Tian, Xuan; Hu, Zhide; Chen, Xingguo

    2004-03-01

    The binding of kaempferol with bovine serum albumin (BSA) was investigated at three temperatures, 296, 310 and 318 K, by the fluorescence, circular dichroism (CD) and Fourier transform infrared spectroscopy (FT-IR) at pH 7.40. The CD and FT-IR studies indicate that kaempferol binds strongly to BSA. The association constant K was determined by Stern-Volmer equation based on the quenching of the fluorescence BSA in the presence of kaempferol. The thermodynamic parameters were calculated according to the dependence of enthalpy change on the temperature as follows: Δ H0 and Δ S0 possess small negative (-1.694 kJ/mol) and positive values (88.814 J/mol K), respectively. According to the displacement experimental and the thermodynamic results, it is considered that kaempferol binding site II (subdomain III) mainly by hydrophobic interaction. The results studied by FT-IR and CD experiments indicate that the secondary structures of the protein have been changed by the interaction of kaempferol with BSA. The distance between the tryptophan residues in BSA and kaempferol bound to site II was estimated to be 2.78 nm using Foster's equation on the basis of fluorescence energy transfer.

  8. Multivariate Meta-Analysis of Genetic Association Studies: A Simulation Study.

    Science.gov (United States)

    Neupane, Binod; Beyene, Joseph

    2015-01-01

    In a meta-analysis with multiple end points of interests that are correlated between or within studies, multivariate approach to meta-analysis has a potential to produce more precise estimates of effects by exploiting the correlation structure between end points. However, under random-effects assumption the multivariate estimation is more complex (as it involves estimation of more parameters simultaneously) than univariate estimation, and sometimes can produce unrealistic parameter estimates. Usefulness of multivariate approach to meta-analysis of the effects of a genetic variant on two or more correlated traits is not well understood in the area of genetic association studies. In such studies, genetic variants are expected to roughly maintain Hardy-Weinberg equilibrium within studies, and also their effects on complex traits are generally very small to modest and could be heterogeneous across studies for genuine reasons. We carried out extensive simulation to explore the comparative performance of multivariate approach with most commonly used univariate inverse-variance weighted approach under random-effects assumption in various realistic meta-analytic scenarios of genetic association studies of correlated end points. We evaluated the performance with respect to relative mean bias percentage, and root mean square error (RMSE) of the estimate and coverage probability of corresponding 95% confidence interval of the effect for each end point. Our simulation results suggest that multivariate approach performs similarly or better than univariate method when correlations between end points within or between studies are at least moderate and between-study variation is similar or larger than average within-study variation for meta-analyses of 10 or more genetic studies. Multivariate approach produces estimates with smaller bias and RMSE especially for the end point that has randomly or informatively missing summary data in some individual studies, when the missing data

  9. ks: Kernel Density Estimation and Kernel Discriminant Analysis for Multivariate Data in R

    Directory of Open Access Journals (Sweden)

    Tarn Duong

    2007-09-01

    Full Text Available Kernel smoothing is one of the most widely used non-parametric data smoothing techniques. We introduce a new R package ks for multivariate kernel smoothing. Currently it contains functionality for kernel density estimation and kernel discriminant analysis. It is a comprehensive package for bandwidth matrix selection, implementing a wide range of data-driven diagonal and unconstrained bandwidth selectors.

  10. Principal response curves: analysis of time-dependent multivariate responses of biological community to stress

    NARCIS (Netherlands)

    Brink, van den P.J.; Braak, ter C.J.F.

    1999-01-01

    In this paper a novel multivariate method is proposed for the analysis of community response data from designed experiments repeatedly sampled in time. The long-term effects of the insecticide chlorpyrifos on the invertebrate community and the dissolved oxygen (DO)–pH–alkalinity–conductivity syndrom

  11. Dynamic factor analysis in the frequency domain: causal modeling of multivariate psychophysiological time series

    NARCIS (Netherlands)

    Molenaar, P.C.M.

    1987-01-01

    Outlines a frequency domain analysis of the dynamic factor model and proposes a solution to the problem of constructing a causal filter of lagged factor loadings. The method is illustrated with applications to simulated and real multivariate time series. The latter applications involve topographic a

  12. Testing key predictions of the associative account of mirror neurons in humans using multivariate pattern analysis.

    Science.gov (United States)

    Oosterhof, Nikolaas N; Wiggett, Alison J; Cross, Emily S

    2014-04-01

    Cook et al. overstate the evidence supporting their associative account of mirror neurons in humans: most studies do not address a key property, action-specificity that generalizes across the visual and motor domains. Multivariate pattern analysis (MVPA) of neuroimaging data can address this concern, and we illustrate how MVPA can be used to test key predictions of their account.

  13. A Multivariate Model for the Meta-Analysis of Study Level Survival Data at Multiple Times

    Science.gov (United States)

    Jackson, Dan; Rollins, Katie; Coughlin, Patrick

    2014-01-01

    Motivated by our meta-analytic dataset involving survival rates after treatment for critical leg ischemia, we develop and apply a new multivariate model for the meta-analysis of study level survival data at multiple times. Our data set involves 50 studies that provide mortality rates at up to seven time points, which we model simultaneously, and…

  14. MULTIVARIATE STATISTICAL ANALYSIS OF CONSUMERS’ PREFERENCE AT THE RUSSIAN MARKET OF CULTURED MILK PRODUCT

    Directory of Open Access Journals (Sweden)

    Lyubov V. Ruchinskaya

    2013-01-01

    Full Text Available Methodological and methodical basis of the developed methods of the multivariate statistical analysis of consumers’ preferences at the Russian market of cultured milk products is considered. The author carried out segmentation of consumers of the cultured milk production based on methods of multidimensional classification and allowing optimizing structure of production of milk production by domestic producers.

  15. Missing Data and Multiple Imputation in the Context of Multivariate Analysis of Variance

    Science.gov (United States)

    Finch, W. Holmes

    2016-01-01

    Multivariate analysis of variance (MANOVA) is widely used in educational research to compare means on multiple dependent variables across groups. Researchers faced with the problem of missing data often use multiple imputation of values in place of the missing observations. This study compares the performance of 2 methods for combining p values in…

  16. Denial-of-service attack detection based on multivariate correlation analysis

    NARCIS (Netherlands)

    Tan, Zhiyuan; Jamdagni, Aruna; He, Xiangjian; Nanda, Priyadarsi; Liu, Ren Ping; Lu, Bao-Liang; Zhang, Liqing; Kwok, James

    2011-01-01

    The reliability and availability of network services are being threatened by the growing number of Denial-of-Service (DoS) attacks. Effective mechanisms for DoS attack detection are demanded. Therefore, we propose a multivariate correlation analysis approach to investigate and extract second-order s

  17. Missing Data and Multiple Imputation in the Context of Multivariate Analysis of Variance

    Science.gov (United States)

    Finch, W. Holmes

    2016-01-01

    Multivariate analysis of variance (MANOVA) is widely used in educational research to compare means on multiple dependent variables across groups. Researchers faced with the problem of missing data often use multiple imputation of values in place of the missing observations. This study compares the performance of 2 methods for combining p values in…

  18. Comparison of pure laparoscopic versus open left hemihepatectomy by multivariate analysis: a retrospective cohort study.

    Science.gov (United States)

    Cho, Hwui-Dong; Kim, Ki-Hun; Hwang, Shin; Ahn, Chul-Soo; Moon, Deok-Bog; Ha, Tae-Yong; Song, Gi-Won; Jung, Dong-Hwan; Park, Gil-Chun; Lee, Sung-Gyu

    2017-07-21

    To compare the outcomes of pure laparoscopic left hemihepatectomy (LLH) versus open left hemihepatectomy (OLH) for benign and malignant conditions using multivariate analysis. All consecutive cases of LLH and OLH between October 2007 and December 2013 in a tertiary referral hospital were enrolled in this retrospective cohort study. All surgical procedures were performed by one surgeon. The LLH and OLH groups were compared in terms of patient demographics, preoperative data, clinical perioperative outcomes, and tumor characteristics in patients with malignancy. Multivariate analysis of the prognostic factors associated with severe complications was then performed. The LLH group (n = 62) had a significantly shorter postoperative hospital stay than the OLH group (n = 118) (9.53 ± 3.30 vs 14.88 ± 11.36 days, p Multivariate analysis revealed that the OLH group had >4 times the risk of the LLH group in terms of developing severe complications (Clavien-Dindo grade ≥III) (odds ratio 4.294, 95% confidence intervals 1.165-15.832, p = 0.029). LLH was a safe and feasible procedure for selected patients. LLH required shorter hospital stay and resulted in less operative blood loss. Multivariate analysis revealed that LLH was associated with a lower risk of severe complications compared to OLH. The authors suggest that LLH could be a reasonable treatment option for selected patients.

  19. Testing key predictions of the associative account of mirror neurons in humans using multivariate pattern analysis

    NARCIS (Netherlands)

    Oosterhof, N.N.; Wiggett, AJ.; Cross, E.S.

    2014-01-01

    Cook et al. overstate the evidence supporting their associative account of mirror neurons in humans: most studies do not address a key property, action-specificity that generalizes across the visual and motor domains. Multivariate pattern analysis (MVPA) of neuroimaging data can address this

  20. Dissection of genomic correlation matrices of US Holsteins using multivariate factor analysis

    Science.gov (United States)

    Aim of the study was to compare correlation matrices between direct genomic predictions for 31 production, fitness and conformation traits both at genomic and chromosomal level in US Holstein bulls. Multivariate factor analysis was used to quantify basic features of correlation matrices. Factor extr...

  1. Multivariate meta-analysis: a robust approach based on the theory of U-statistic.

    Science.gov (United States)

    Ma, Yan; Mazumdar, Madhu

    2011-10-30

    Meta-analysis is the methodology for combining findings from similar research studies asking the same question. When the question of interest involves multiple outcomes, multivariate meta-analysis is used to synthesize the outcomes simultaneously taking into account the correlation between the outcomes. Likelihood-based approaches, in particular restricted maximum likelihood (REML) method, are commonly utilized in this context. REML assumes a multivariate normal distribution for the random-effects model. This assumption is difficult to verify, especially for meta-analysis with small number of component studies. The use of REML also requires iterative estimation between parameters, needing moderately high computation time, especially when the dimension of outcomes is large. A multivariate method of moments (MMM) is available and is shown to perform equally well to REML. However, there is a lack of information on the performance of these two methods when the true data distribution is far from normality. In this paper, we propose a new nonparametric and non-iterative method for multivariate meta-analysis on the basis of the theory of U-statistic and compare the properties of these three procedures under both normal and skewed data through simulation studies. It is shown that the effect on estimates from REML because of non-normal data distribution is marginal and that the estimates from MMM and U-statistic-based approaches are very similar. Therefore, we conclude that for performing multivariate meta-analysis, the U-statistic estimation procedure is a viable alternative to REML and MMM. Easy implementation of all three methods are illustrated by their application to data from two published meta-analysis from the fields of hip fracture and periodontal disease. We discuss ideas for future research based on U-statistic for testing significance of between-study heterogeneity and for extending the work to meta-regression setting.

  2. Study of ionically modified water performance in carbonate reservoir system by multivariate data analysis

    DEFF Research Database (Denmark)

    Sohal, Muhammad Adeel Nassar; Kucheryavskiy, Sergey V.; Thyne, Geoffrey

    2017-01-01

    in other cases. Most of the published results attributed EOR to improved water wetness in initially oil-wet carbonates. Nevertheless, in a few studies EOR was observed without apparent wettability alteration. We undertake the analysis of a large set of published recovery experiments to try to identify...... the critical mechanisms at the pore scale. Better pore scale physico-chemical understanding will guide to formulate accurate reservoir-scale models. This paper presents a comprehensive meta-analysis of the proposed mechanisms using multivariate data analysis. Detailed review of the subject, including...... mechanisms with supporting and contradictory evidence has been presented by Sohal et al. (2016). In this study, the significance of each contributing factor to EOR was quantified and subjected to rigorous multivariate statistical analysis. The analysis was limited because there is no uniform methodology...

  3. Kaempferol as Selective Human MAO-A Inhibitor: Analytical Detection in Calabrian Red Wines, Biological and Molecular Modeling Studies.

    Science.gov (United States)

    Gidaro, Maria Concetta; Astorino, Christian; Petzer, Anél; Carradori, Simone; Alcaro, Francesca; Costa, Giosuè; Artese, Anna; Rafele, Giancarlo; Russo, Francesco M; Petzer, Jacobus P; Alcaro, Stefano

    2016-02-17

    The purpose of this work was to determine the kaempferol content in three red wines of Calabria, a southern Italian region with a great number of certified food products. Considering that wine cultivar, climate, and soil influence the qualitative and quantitative composition in flavonoids of Vitis vinifera L. berries, the three analyzed samples were taken from the 2013 vintage. Moreover, the Gaglioppo samples, with assigned Controlled Origin Denomination (DOC), were also investigated in the production of years 2008, 2010, and 2011. In addition to the analysis of kaempferol, which is present in higher concentration than in other Italian wines, in vitro assays were performed to evaluate, for the first time, the inhibition of the human monoamine oxidases (hMAO-A and hMAO-B). Molecular recognition studies were also carried out to provide insight into the binding mode of kaempferol and selectivity of inhibition of the hMAO-A isoform.

  4. Multivariate analysis of Ion Beam Induced Luminescence spectra of irradiated silver ion-exchanged silicate glasses

    Science.gov (United States)

    Valotto, Gabrio; Quaranta, Alberto; Cattaruzza, Elti; Gonella, Francesco; Rampazzo, Giancarlo

    A multivariate analysis is used for the identification of the spectral features in Ion Beam Induced Luminescence (IBIL) spectra of soda-lime silicate glasses doped with silver by Ag+-Na+ ion exchange. Both Principal Component Analysis and multivariate analysis were used to characterize time-evolving IBIL spectra of Ag-doped glasses, by means of the identification of the number and of the wavelength positions of the main luminescent features and the study of their evolution during irradiation. This method helps to identify the spectral features of the samples spectra, even when partially overlapped or less intense. This analysis procedure does not require additional input such as the number of peaks.

  5. MGAS: a powerful tool for multivariate gene-based genome-wide association analysis.

    Science.gov (United States)

    Van der Sluis, Sophie; Dolan, Conor V; Li, Jiang; Song, Youqiang; Sham, Pak; Posthuma, Danielle; Li, Miao-Xin

    2015-04-01

    Standard genome-wide association studies, testing the association between one phenotype and a large number of single nucleotide polymorphisms (SNPs), are limited in two ways: (i) traits are often multivariate, and analysis of composite scores entails loss in statistical power and (ii) gene-based analyses may be preferred, e.g. to decrease the multiple testing problem. Here we present a new method, multivariate gene-based association test by extended Simes procedure (MGAS), that allows gene-based testing of multivariate phenotypes in unrelated individuals. Through extensive simulation, we show that under most trait-generating genotype-phenotype models MGAS has superior statistical power to detect associated genes compared with gene-based analyses of univariate phenotypic composite scores (i.e. GATES, multiple regression), and multivariate analysis of variance (MANOVA). Re-analysis of metabolic data revealed 32 False Discovery Rate controlled genome-wide significant genes, and 12 regions harboring multiple genes; of these 44 regions, 30 were not reported in the original analysis. MGAS allows researchers to conduct their multivariate gene-based analyses efficiently, and without the loss of power that is often associated with an incorrectly specified genotype-phenotype models. MGAS is freely available in KGG v3.0 (http://statgenpro.psychiatry.hku.hk/limx/kgg/download.php). Access to the metabolic dataset can be requested at dbGaP (https://dbgap.ncbi.nlm.nih.gov/). The R-simulation code is available from http://ctglab.nl/people/sophie_van_der_sluis. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press.

  6. Multivariate meta-analysis for non-linear and other multi-parameter associations

    Science.gov (United States)

    Gasparrini, A; Armstrong, B; Kenward, M G

    2012-01-01

    In this paper, we formalize the application of multivariate meta-analysis and meta-regression to synthesize estimates of multi-parameter associations obtained from different studies. This modelling approach extends the standard two-stage analysis used to combine results across different sub-groups or populations. The most straightforward application is for the meta-analysis of non-linear relationships, described for example by regression coefficients of splines or other functions, but the methodology easily generalizes to any setting where complex associations are described by multiple correlated parameters. The modelling framework of multivariate meta-analysis is implemented in the package mvmeta within the statistical environment R. As an illustrative example, we propose a two-stage analysis for investigating the non-linear exposure–response relationship between temperature and non-accidental mortality using time-series data from multiple cities. Multivariate meta-analysis represents a useful analytical tool for studying complex associations through a two-stage procedure. Copyright © 2012 John Wiley & Sons, Ltd. PMID:22807043

  7. Multivariate pattern analysis of MEG and EEG: A comparison of representational structure in time and space.

    Science.gov (United States)

    Cichy, Radoslaw Martin; Pantazis, Dimitrios

    2017-07-14

    Multivariate pattern analysis of magnetoencephalography (MEG) and electroencephalography (EEG) data can reveal the rapid neural dynamics underlying cognition. However, MEG and EEG have systematic differences in sampling neural activity. This poses the question to which degree such measurement differences consistently bias the results of multivariate analysis applied to MEG and EEG activation patterns. To investigate, we conducted a concurrent MEG/EEG study while participants viewed images of everyday objects. We applied multivariate classification analyses to MEG and EEG data, and compared the resulting time courses to each other, and to fMRI data for an independent evaluation in space. We found that both MEG and EEG revealed the millisecond spatio-temporal dynamics of visual processing with largely equivalent results. Beyond yielding convergent results, we found that MEG and EEG also captured partly unique aspects of visual representations. Those unique components emerged earlier in time for MEG than for EEG. Identifying the sources of those unique components with fMRI, we found the locus for both MEG and EEG in high-level visual cortex, and in addition for MEG in low-level visual cortex. Together, our results show that multivariate analyses of MEG and EEG data offer a convergent and complimentary view on neural processing, and motivate the wider adoption of these methods in both MEG and EEG research. Copyright © 2017 Elsevier Inc. All rights reserved.

  8. Extending Inferential Group Analysis in Type 2 Diabetic Patients with Multivariate GLM Implemented in SPM8.

    Science.gov (United States)

    Ferreira, Fábio S; Pereira, João M S; Duarte, João V; Castelo-Branco, Miguel

    2017-01-01

    Although voxel based morphometry studies are still the standard for analyzing brain structure, their dependence on massive univariate inferential methods is a limiting factor. A better understanding of brain pathologies can be achieved by applying inferential multivariate methods, which allow the study of multiple dependent variables, e.g. different imaging modalities of the same subject. Given the widespread use of SPM software in the brain imaging community, the main aim of this work is the implementation of massive multivariate inferential analysis as a toolbox in this software package. applied to the use of T1 and T2 structural data from diabetic patients and controls. This implementation was compared with the traditional ANCOVA in SPM and a similar multivariate GLM toolbox (MRM). We implemented the new toolbox and tested it by investigating brain alterations on a cohort of twenty-eight type 2 diabetes patients and twenty-six matched healthy controls, using information from both T1 and T2 weighted structural MRI scans, both separately - using standard univariate VBM - and simultaneously, with multivariate analyses. Univariate VBM replicated predominantly bilateral changes in basal ganglia and insular regions in type 2 diabetes patients. On the other hand, multivariate analyses replicated key findings of univariate results, while also revealing the thalami as additional foci of pathology. While the presented algorithm must be further optimized, the proposed toolbox is the first implementation of multivariate statistics in SPM8 as a user-friendly toolbox, which shows great potential and is ready to be validated in other clinical cohorts and modalities.

  9. TOF-SIMS analysis of polystyrene/polybutadiene blend using chemical derivatization and multivariate analysis

    Science.gov (United States)

    Kono, Teiichiro; Iwase, Eijiro; Kanamori, Yukiko

    2008-12-01

    Chemical imaging with high spatial resolution is one of the features of TOF-SIMS. However, degradation of the sample due to primary ion bombardment becomes problematic when the analysis area is small. Although polystyrene (PS) and polybutadiene (PB) separately show relatively distinct spectra, observation of their phase separation in PS/PB blends is difficult when the analysis area is small because degradation of both polymers and especially PS leads to disappearance of their characteristic peaks, resulting in low chemical image contrast. We therefore investigated the application of various forms of multivariate analysis (MVA) to the TOF-SIMS image data to improve the chemical image contrast. PCA, MCR, and the other forms of MVA provided improvement in contrast, but the images were still obscure and observation of phase separation remained difficult. Chemical derivatization using osmium tetroxide was also investigated, and found to give clear images of phase separation in the PS/PB blend. In quantitative determinations with MVA and chemical derivatization, PLS demonstrated the best predictive capability and chemical derivatization resulted in large deviations from both the bulk chemical composition and the determinations with MVA, particularly in regions of low PB content.

  10. Multivariation calibration techniques applied to NIRA (near infrared reflectance analysis) and FTIR (Fourier transform infrared) data

    Science.gov (United States)

    Long, C. L.

    1991-02-01

    Multivariate calibration techniques can reduce the time required for routine testing and can provide new methods of analysis. Multivariate calibration is commonly used with near infrared reflectance analysis (NIRA) and Fourier transform infrared (FTIR) spectroscopy. Two feasibility studies were performed to determine the capability of NIRA, using multivariate calibration techniques, to perform analyses on the types of samples that are routinely analyzed at this laboratory. The first study performed included a variety of samples and indicated that NIRA would be well-suited to perform analyses on selected materials properties such as water content and hydroxyl number on polyol samples, epoxy content on epoxy resins, water content of desiccants, and the amine values of various amine cure agents. A second study was performed to assess the capability of NIRA to perform quantitative analysis of hydroxyl numbers and water contents of hydroxyl-containing materials. Hydroxyl number and water content were selected for determination because these tests are frequently run on polyol materials and the hydroxyl number determination is time consuming. This study pointed out the necessity of obtaining calibration standards identical to the samples being analyzed for each type of polyol or other material being analyzed. Multivariate calibration techniques are frequently used with FTIR data to determine the composition of a large variety of complex mixtures. A literature search indicated many applications of multivariate calibration to FTIR data. Areas identified where quantitation by FTIR would provide a new capability are quantitation of components in epoxy and silicone resins, polychlorinated biphenyls (PCBs) in oils, and additives to polymers.

  11. Interpretability of Multivariate Brain Maps in Linear Brain Decoding: Definition, and Heuristic Quantification in Multivariate Analysis of MEG Time-Locked Effects.

    Science.gov (United States)

    Kia, Seyed Mostafa; Vega Pons, Sandro; Weisz, Nathan; Passerini, Andrea

    2016-01-01

    Brain decoding is a popular multivariate approach for hypothesis testing in neuroimaging. Linear classifiers are widely employed in the brain decoding paradigm to discriminate among experimental conditions. Then, the derived linear weights are visualized in the form of multivariate brain maps to further study spatio-temporal patterns of underlying neural activities. It is well known that the brain maps derived from weights of linear classifiers are hard to interpret because of high correlations between predictors, low signal to noise ratios, and the high dimensionality of neuroimaging data. Therefore, improving the interpretability of brain decoding approaches is of primary interest in many neuroimaging studies. Despite extensive studies of this type, at present, there is no formal definition for interpretability of multivariate brain maps. As a consequence, there is no quantitative measure for evaluating the interpretability of different brain decoding methods. In this paper, first, we present a theoretical definition of interpretability in brain decoding; we show that the interpretability of multivariate brain maps can be decomposed into their reproducibility and representativeness. Second, as an application of the proposed definition, we exemplify a heuristic for approximating the interpretability in multivariate analysis of evoked magnetoencephalography (MEG) responses. Third, we propose to combine the approximated interpretability and the generalization performance of the brain decoding into a new multi-objective criterion for model selection. Our results, for the simulated and real MEG data, show that optimizing the hyper-parameters of the regularized linear classifier based on the proposed criterion results in more informative multivariate brain maps. More importantly, the presented definition provides the theoretical background for quantitative evaluation of interpretability, and hence, facilitates the development of more effective brain decoding algorithms

  12. Interpretability of Multivariate Brain Maps in Linear Brain Decoding: Definition, and Heuristic Quantification in Multivariate Analysis of MEG Time-Locked Effects

    Science.gov (United States)

    Kia, Seyed Mostafa; Vega Pons, Sandro; Weisz, Nathan; Passerini, Andrea

    2017-01-01

    Brain decoding is a popular multivariate approach for hypothesis testing in neuroimaging. Linear classifiers are widely employed in the brain decoding paradigm to discriminate among experimental conditions. Then, the derived linear weights are visualized in the form of multivariate brain maps to further study spatio-temporal patterns of underlying neural activities. It is well known that the brain maps derived from weights of linear classifiers are hard to interpret because of high correlations between predictors, low signal to noise ratios, and the high dimensionality of neuroimaging data. Therefore, improving the interpretability of brain decoding approaches is of primary interest in many neuroimaging studies. Despite extensive studies of this type, at present, there is no formal definition for interpretability of multivariate brain maps. As a consequence, there is no quantitative measure for evaluating the interpretability of different brain decoding methods. In this paper, first, we present a theoretical definition of interpretability in brain decoding; we show that the interpretability of multivariate brain maps can be decomposed into their reproducibility and representativeness. Second, as an application of the proposed definition, we exemplify a heuristic for approximating the interpretability in multivariate analysis of evoked magnetoencephalography (MEG) responses. Third, we propose to combine the approximated interpretability and the generalization performance of the brain decoding into a new multi-objective criterion for model selection. Our results, for the simulated and real MEG data, show that optimizing the hyper-parameters of the regularized linear classifier based on the proposed criterion results in more informative multivariate brain maps. More importantly, the presented definition provides the theoretical background for quantitative evaluation of interpretability, and hence, facilitates the development of more effective brain decoding algorithms

  13. Dynamic molecular monitoring of retina inflammation by in vivo Raman spectroscopy coupled with multivariate analysis.

    Science.gov (United States)

    Marro, Monica; Taubes, Alice; Abernathy, Alice; Balint, Stephan; Moreno, Beatriz; Sanchez-Dalmau, Bernardo; Martínez-Lapiscina, Elena H; Amat-Roldan, Ivan; Petrov, Dmitri; Villoslada, Pablo

    2014-09-01

    Retinal tissue is damaged during inflammation in Multiple Sclerosis. We assessed molecular changes in inflamed murine retinal cultures by Raman spectroscopy. Partial Least Squares-Discriminant analysis (PLS-DA) was able to classify retina cultures as inflamed with high accuracy. Using Multivariate Curve Resolution (MCR) analysis, we deconvolved 6 molecular components suffering dynamic changes along inflammatory process. Those include the increase of immune mediators (Lipoxygenase, iNOS and TNFα), changes in molecules involved in energy production (Cytochrome C, phenylalanine and NADH/NAD+) and decrease of Phosphatidylcholine. Raman spectroscopy combined with multivariate analysis allows monitoring the evolution of retina inflammation. Copyright © 2014 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim.

  14. Multivariate Analysis of Soil Heavy Metals Pollution Along Irbid – Zarqa Highway

    Directory of Open Access Journals (Sweden)

    Sana’a Odat

    2012-10-01

    Full Text Available Problem statement: In this study, selected statistical methods (Correlation analysis, Principal component analysis and Multivariate analysis were used to determine the heavy metal accumulation and itscontrolling factor and to identify the origin of these metals in soil samples collected from sediment of Irbid, Jordan. Approach: Twenty one soil samples were collected and analyzed in the laboratory for some heavy metals by atomic absorption Spectrophotometric method and multivariate statistical techniques. Results: The overall decreasing metal concentration order was: Fe > K > Mg > Mn >Na > Cu >Pb > Zn . Significantly positive correlation was only found between Cu, Mn and Zn in one hand and between PH and NO3 in the other hand. Factor analysis shows that sediment quality data consists of four major components accounting for 74.982% of cumulative variance of the contamination: Conclusion: This study concluded that the concentrations of all metals measured in Irbid can be considered to present a low level of contamination and that multivariate statistical analysis is a useful tool in understanding contaminants relationships.

  15. Unified structural equation modeling approach for the analysis of multisubject, multivariate functional MRI data.

    Science.gov (United States)

    Kim, Jieun; Zhu, Wei; Chang, Linda; Bentler, Peter M; Ernst, Thomas

    2007-02-01

    The ultimate goal of brain connectivity studies is to propose, test, modify, and compare certain directional brain pathways. Path analysis or structural equation modeling (SEM) is an ideal statistical method for such studies. In this work, we propose a two-stage unified SEM plus GLM (General Linear Model) approach for the analysis of multisubject, multivariate functional magnetic resonance imaging (fMRI) time series data with subject-level covariates. In Stage 1, we analyze the fMRI multivariate time series for each subject individually via a unified SEM model by combining longitudinal pathways represented by a multivariate autoregressive (MAR) model, and contemporaneous pathways represented by a conventional SEM. In Stage 2, the resulting subject-level path coefficients are merged with subject-level covariates such as gender, age, IQ, etc., to examine the impact of these covariates on effective connectivity via a GLM. Our approach is exemplified via the analysis of an fMRI visual attention experiment. Furthermore, the significant path network from the unified SEM analysis is compared to that from a conventional SEM analysis without incorporating the longitudinal information as well as that from a Dynamic Causal Modeling (DCM) approach.

  16. Development of Validated High-performance Thin-layer Chromatography Method for Simultaneous Determination of Quercetin and Kaempferol in Thespesia populnea.

    Science.gov (United States)

    Panchal, Hiteksha; Amin, Aeshna; Shah, Mamta

    2017-01-01

    Thespesia populnea L. (Family: Malvaceae) is a well-known medicinal plant distributed in tropical regions of the world and cultivated in South Gujarat and indicated to be useful in cutaneous affections, psoriasis, ringworm, and eczema. Bark and fruits are indicated in the diseases of skin, urethritis, and gonorrhea. The juice of fruits is employed in treating certain hepatic diseases. The plant is reported to contain flavonoids, quercetin, kaempferol, gossypetin, Kaempferol-3-monoglucoside, β-sitosterol, kaempferol-7-glucoside, and gossypol. T. populnea is a common component of many herbal and Ayurvedic formulation such as Kamilari and Liv-52. The present study aimed at developing validated and reliable high-performance thin layer chromatography (HPTLC) method for the analysis of quercetin and kaempferol simultaneously in T. populnea. The method employed thin-layer chromatography aluminum sheets precoated with silica gel as the stationary phase and toluene: ethyl acetate: formic acid (6:4:0.3 v/v/v) as the mobile phase, which gave compact bands of quercetin and kaempferol. Linear regression data for the calibration curves of standard quercetin and kaempferol showed a good linear relationship over a concentration range of 100-600 ng/spot and 500-3000 ng/spot with respect to the area and correlation coefficient (R2) was 0.9955 and 0.9967. The method was evaluated regarding accuracy, precision, selectivity, and robustness. Limits of detection and quantitation were recorded as 32.06 and 85.33 ng/spot and 74.055 and 243.72 ng/spot for quercetin and kaempferol, respectively. We concluded that this method employing HPTLC in the quantitative determination of quercetin and kaempferol is efficient, simple, accurate, and validated.

  17. Mapping informative clusters in a hierarchical [corrected] framework of FMRI multivariate analysis.

    Directory of Open Access Journals (Sweden)

    Rui Xu

    Full Text Available Pattern recognition methods have become increasingly popular in fMRI data analysis, which are powerful in discriminating between multi-voxel patterns of brain activities associated with different mental states. However, when they are used in functional brain mapping, the location of discriminative voxels varies significantly, raising difficulties in interpreting the locus of the effect. Here we proposed a hierarchical framework of multivariate approach that maps informative clusters rather than voxels to achieve reliable functional brain mapping without compromising the discriminative power. In particular, we first searched for local homogeneous clusters that consisted of voxels with similar response profiles. Then, a multi-voxel classifier was built for each cluster to extract discriminative information from the multi-voxel patterns. Finally, through multivariate ranking, outputs from the classifiers were served as a multi-cluster pattern to identify informative clusters by examining interactions among clusters. Results from both simulated and real fMRI data demonstrated that this hierarchical approach showed better performance in the robustness of functional brain mapping than traditional voxel-based multivariate methods. In addition, the mapped clusters were highly overlapped for two perceptually equivalent object categories, further confirming the validity of our approach. In short, the hierarchical framework of multivariate approach is suitable for both pattern classification and brain mapping in fMRI studies.

  18. Estimating multivariate similarity between neuroimaging datasets with sparse canonical correlation analysis: an application to perfusion imaging.

    Science.gov (United States)

    Rosa, Maria J; Mehta, Mitul A; Pich, Emilio M; Risterucci, Celine; Zelaya, Fernando; Reinders, Antje A T S; Williams, Steve C R; Dazzan, Paola; Doyle, Orla M; Marquand, Andre F

    2015-01-01

    An increasing number of neuroimaging studies are based on either combining more than one data modality (inter-modal) or combining more than one measurement from the same modality (intra-modal). To date, most intra-modal studies using multivariate statistics have focused on differences between datasets, for instance relying on classifiers to differentiate between effects in the data. However, to fully characterize these effects, multivariate methods able to measure similarities between datasets are needed. One classical technique for estimating the relationship between two datasets is canonical correlation analysis (CCA). However, in the context of high-dimensional data the application of CCA is extremely challenging. A recent extension of CCA, sparse CCA (SCCA), overcomes this limitation, by regularizing the model parameters while yielding a sparse solution. In this work, we modify SCCA with the aim of facilitating its application to high-dimensional neuroimaging data and finding meaningful multivariate image-to-image correspondences in intra-modal studies. In particular, we show how the optimal subset of variables can be estimated independently and we look at the information encoded in more than one set of SCCA transformations. We illustrate our framework using Arterial Spin Labeling data to investigate multivariate similarities between the effects of two antipsychotic drugs on cerebral blood flow.

  19. Fresh Biomass Estimation in Heterogeneous Grassland Using Hyperspectral Measurements and Multivariate Statistical Analysis

    Science.gov (United States)

    Darvishzadeh, R.; Skidmore, A. K.; Mirzaie, M.; Atzberger, C.; Schlerf, M.

    2014-12-01

    Accurate estimation of grassland biomass at their peak productivity can provide crucial information regarding the functioning and productivity of the rangelands. Hyperspectral remote sensing has proved to be valuable for estimation of vegetation biophysical parameters such as biomass using different statistical techniques. However, in statistical analysis of hyperspectral data, multicollinearity is a common problem due to large amount of correlated hyper-spectral reflectance measurements. The aim of this study was to examine the prospect of above ground biomass estimation in a heterogeneous Mediterranean rangeland employing multivariate calibration methods. Canopy spectral measurements were made in the field using a GER 3700 spectroradiometer, along with concomitant in situ measurements of above ground biomass for 170 sample plots. Multivariate calibrations including partial least squares regression (PLSR), principal component regression (PCR), and Least-Squared Support Vector Machine (LS-SVM) were used to estimate the above ground biomass. The prediction accuracy of the multivariate calibration methods were assessed using cross validated R2 and RMSE. The best model performance was obtained using LS_SVM and then PLSR both calibrated with first derivative reflectance dataset with R2cv = 0.88 & 0.86 and RMSEcv= 1.15 & 1.07 respectively. The weakest prediction accuracy was appeared when PCR were used (R2cv = 0.31 and RMSEcv= 2.48). The obtained results highlight the importance of multivariate calibration methods for biomass estimation when hyperspectral data are used.

  20. Multivariate data analysis for finding the relevant fatty acids contributing to the melting fractions of cream

    DEFF Research Database (Denmark)

    Buldo, Patrizia; Larsen, Mette Krogh; Wiking, Lars

    2013-01-01

    BACKGROUND: The melting behaviour and fatty acid composition of cream from a total of 33 cows from four farms were analysed. Multivariate data analysis was used to identify the fatty acids that contributed most to the melting points and to differentiate between creams from different practical......:0 and palmitoleic acid (C16:1) in milk fat, whereas it decreased the amount of stearic acid (C18:0) and C18:1 trans fatty acid. Average data on the melting behaviour of cream separated the farms into two groups where the main differences in feeding were the amounts of maize silage and rapeseed cake used. CONCLUSION......: Multivariate analysis of data from individual cows identified the most relevant fatty acids contributing to the melting point of the medium melting fraction of cream. The fatty acid composition of milk fat could differentiate cream from different feeding strategies; however, owing to individual cow variation...

  1. Fluorescence measurements for evaluating the application of multivariate analysis techniques to optically thick environments.

    Energy Technology Data Exchange (ETDEWEB)

    Reichardt, Thomas A.; Timlin, Jerilyn Ann; Jones, Howland D. T.; Sickafoose, Shane M.; Schmitt, Randal L.

    2010-09-01

    Laser-induced fluorescence measurements of cuvette-contained laser dye mixtures are made for evaluation of multivariate analysis techniques to optically thick environments. Nine mixtures of Coumarin 500 and Rhodamine 610 are analyzed, as well as the pure dyes. For each sample, the cuvette is positioned on a two-axis translation stage to allow the interrogation at different spatial locations, allowing the examination of both primary (absorption of the laser light) and secondary (absorption of the fluorescence) inner filter effects. In addition to these expected inner filter effects, we find evidence that a portion of the absorbed fluorescence is re-emitted. A total of 688 spectra are acquired for the evaluation of multivariate analysis approaches to account for nonlinear effects.

  2. Authentication of Trappist beers by LC-MS fingerprints and multivariate data analysis.

    Science.gov (United States)

    Mattarucchi, Elia; Stocchero, Matteo; Moreno-Rojas, José Manuel; Giordano, Giuseppe; Reniero, Fabiano; Guillou, Claude

    2010-12-08

    The aim of this study was to asses the applicability of LC-MS profiling to authenticate a selected Trappist beer as part of a program on traceability funded by the European Commission. A total of 232 beers were fingerprinted and classified through multivariate data analysis. The selected beer was clearly distinguished from beers of different brands, while only 3 samples (3.5% of the test set) were wrongly classified when compared with other types of beer of the same Trappist brewery. The fingerprints were further analyzed to extract the most discriminating variables, which proved to be sufficient for classification, even using a simplified unsupervised model. This reduced fingerprint allowed us to study the influence of batch-to-batch variability on the classification model. Our results can easily be applied to different matrices and they confirmed the effectiveness of LC-MS profiling in combination with multivariate data analysis for the characterization of food products.

  3. Cytoprotective effect of kaempferol against palmitic acid-induced pancreatic β-cell death through modulation of autophagy via AMPK/mTOR signaling pathway.

    Science.gov (United States)

    Varshney, Ritu; Gupta, Sumeet; Roy, Partha

    2017-02-22

    Lipotoxicity of pancreatic β-cells is the pathological manifestation of obesity-linked type II diabetes. We intended to determine the cytoprotective effect of kaempferol on pancreatic β-cells undergoing apoptosis in palmitic acid (PA)-stressed condition. The data showed that kaempferol treatment increased cell viability and anti-apoptotic activity in PA-stressed RIN-5F cells and murine pancreatic islets. Furthermore, kaempferol's ability to instigate autophagy was illustrated by MDC-LysoTracker red staining and TEM analysis which corroborated well with the observed increase in LC3 puncta and LC3-II protein expressions along with the concomitant decline in p62 expression. Apart from this, the data showed that kaempferol up/down-regulates AMPK/mTOR phosphorylation respectively. Subsequently, upon inhibition of AMPK phosphorylation by AMPK inhibitors, kaempferol mediated autophagy was abolished which further led to the decline in β-cell survival. Such observations collectively lead to the conclusion that, kaempferol exerts its cytoprotective role against lipotoxicity by activation of autophagy via AMPK/mTOR pathway.

  4. Multivariate analysis of lactose content in milk of Holstein and Jersey cows1

    OpenAIRE

    Dileta Regina Moro Alessio; André Thaler Neto; João Pedro Velho; Ildemar Brayer Perreira; David José Miquelluti; Deise Aline Knob; Claudineli Gasparini da Silva

    2016-01-01

    This study evaluated the factors influencing the variation in the lactose content of milk in Holstein and Jersey herds in Santa Catarina, southern Brazil, using multivariate analysis. Data from 73 dairy herds in the Dairy Herds Improvement Program of the State of Santa Catarina were provided by the Santa Catarina Association of Cattle Breeders (ACCB). A total of 46,242 monthly records of Holstein and Jersey (59 and 41 % of the total records, respectively) cows from 2009 to 2012 were analyzed ...

  5. Causal diagrams and multivariate analysis I: a quiver full of arrows.

    Science.gov (United States)

    Jupiter, Daniel C

    2014-01-01

    How do we know which variables we should include in our multivariate analyses? What role does each variable play in our understanding of the analysis? In this article I begin a discussion of these issues and describe 2 different types of studies for which this problem must be handled in different ways. Copyright © 2014 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

  6. Multivariate Analysis Approach to the Serum Peptide Profile of Morbidly Obese Patients

    Directory of Open Access Journals (Sweden)

    M. Agostini

    2013-01-01

    Full Text Available Background: Obesity is currently epidemic in many countries worldwide and is strongly related to diabetes and cardiovascular disease. Mass spectrometry, in particular matrix-assisted laser desorption/ionization time of flight (MALDI-TOF is currently used for detecting different pattern of expressed protein. This study investigated the differences in low molecular weight (LMW peptide profiles between obese and normal-weight subjects in combination with multivariate statistical analysis.

  7. Multivariate time delay analysis based local KPCA fault prognosis approach for nonlinear processes☆

    Institute of Scientific and Technical Information of China (English)

    Yuan Xu; Ying Liu; Qunxiong Zhu

    2016-01-01

    Currently, some fault prognosis technology occasionally has relatively unsatisfied performance especially for in-cipient faults in nonlinear processes duo to their large time delay and complex internal connection. To overcome this deficiency, multivariate time delay analysis is incorporated into the high sensitive local kernel principal com-ponent analysis. In this approach, mutual information estimation and Bayesian information criterion (BIC) are separately used to acquire the correlation degree and time delay of the process variables. Moreover, in order to achieve prediction, time series prediction by back propagation (BP) network is applied whose input is multivar-iate correlated time series other than the original time series. Then the multivariate time delayed series and future values obtained by time series prediction are combined to construct the input of local kernel principal component analysis (LKPCA) model for incipient fault prognosis. The new method has been exemplified in a sim-ple nonlinear process and the complicated Tennessee Eastman (TE) benchmark process. The results indicate that the new method has superiority in the fault prognosis sensitivity over other traditional fault prognosis methods. © 2016 The Chemical Industry and Engineering Society of China, and Chemical Industry Press. Al rights reserved.

  8. The association between body mass index and severe biliary infections: a multivariate analysis.

    Science.gov (United States)

    Stewart, Lygia; Griffiss, J McLeod; Jarvis, Gary A; Way, Lawrence W

    2012-11-01

    Obesity has been associated with worse infectious disease outcomes. It is a risk factor for cholesterol gallstones, but little is known about associations between body mass index (BMI) and biliary infections. We studied this using factors associated with biliary infections. A total of 427 patients with gallstones were studied. Gallstones, bile, and blood (as applicable) were cultured. Illness severity was classified as follows: none (no infection or inflammation), systemic inflammatory response syndrome (fever, leukocytosis), severe (abscess, cholangitis, empyema), or multi-organ dysfunction syndrome (bacteremia, hypotension, organ failure). Associations between BMI and biliary bacteria, bacteremia, gallstone type, and illness severity were examined using bivariate and multivariate analysis. BMI inversely correlated with pigment stones, biliary bacteria, bacteremia, and increased illness severity on bivariate and multivariate analysis. Obesity correlated with less severe biliary infections. BMI inversely correlated with pigment stones and biliary bacteria; multivariate analysis showed an independent correlation between lower BMI and illness severity. Most patients with severe biliary infections had a normal BMI, suggesting that obesity may be protective in biliary infections. This study examined the correlation between BMI and biliary infection severity. Published by Elsevier Inc.

  9. Risk factors for incidental durotomy during lumbar surgery: a retrospective study by multivariate analysis.

    Science.gov (United States)

    Chen, Zhixiang; Shao, Peng; Sun, Qizhao; Zhao, Dong

    2015-03-01

    The purpose of the present study was to use a prospectively collected data to evaluate the rate of incidental durotomy (ID) during lumbar surgery and determine the associated risk factors by using univariate and multivariate analysis. We retrospectively reviewed 2184 patients who underwent lumbar surgery from January 1, 2009 to December 31, 2011 at a single hospital. Patients with ID (n=97) were compared with the patients without ID (n=2019). The influences of several potential risk factors that might affect the occurrence of ID were assessed using univariate and multivariate analyses. The overall incidence of ID was 4.62%. Univariate analysis demonstrated that older age, diabetes, lumbar central stenosis, posterior approach, revision surgery, prior lumber surgery and minimal invasive surgery are risk factors for ID during lumbar surgery. However, multivariate analysis identified older age, prior lumber surgery, revision surgery, and minimally invasive surgery as independent risk factors. Older age, prior lumber surgery, revision surgery, and minimal invasive surgery were independent risk factors for ID during lumbar surgery. These findings may guide clinicians making future surgical decisions regarding ID and aid in the patient counseling process to alleviate risks and complications. Copyright © 2015 Elsevier B.V. All rights reserved.

  10. Estimation of failure criteria in multivariate sensory shelf life testing using survival analysis.

    Science.gov (United States)

    Giménez, Ana; Gagliardi, Andrés; Ares, Gastón

    2017-09-01

    For most food products, shelf life is determined by changes in their sensory characteristics. A predetermined increase or decrease in the intensity of a sensory characteristic has frequently been used to signal that a product has reached the end of its shelf life. Considering all attributes change simultaneously, the concept of multivariate shelf life allows a single measurement of deterioration that takes into account all these sensory changes at a certain storage time. The aim of the present work was to apply survival analysis to estimate failure criteria in multivariate sensory shelf life testing using two case studies, hamburger buns and orange juice, by modelling the relationship between consumers' rejection of the product and the deterioration index estimated using PCA. In both studies, a panel of 13 trained assessors evaluated the samples using descriptive analysis whereas a panel of 100 consumers answered a "yes" or "no" question regarding intention to buy or consume the product. PC1 explained the great majority of the variance, indicating all sensory characteristics evolved similarly with storage time. Thus, PC1 could be regarded as index of sensory deterioration and a single failure criterion could be estimated through survival analysis for 25 and 50% consumers' rejection. The proposed approach based on multivariate shelf life testing may increase the accuracy of shelf life estimations. Copyright © 2017 Elsevier Ltd. All rights reserved.

  11. [Multivariate analysis of heavy metal element concentrations in atmospheric deposition in Harbin City, northeast China].

    Science.gov (United States)

    Tang, Jie; Han, Wei-Zheng; Li, Na; Li, Zhao-Yang; Bian, Jian-Min; Li, Hai-Yi

    2011-11-01

    In order to understand the characteristics of atmospheric heavy metal deposition in Harbin City, 46 deposition samples were collected which were taken using bulk deposition samplers during the period of 2008-2009 (about 365 days). The samples were analyzed for heavy metal concentration by atomic fluorescence spectrometry (AFS) and inductively coupled plasma-atomic spectrometry (ICP-AES). The deposition flux was calculated. Sources analysis was made by the method of principal component analysis (PCA), Pearsons and enrichment factor (EF). The following points can be gained through multivariate analysis. Mn and Co are mostly from natural sources while the others may be brought by coal dust, vehicle emissions and metal smelting.

  12. Spectral compression algorithms for the analysis of very large multivariate images

    Science.gov (United States)

    Keenan, Michael R.

    2007-10-16

    A method for spectrally compressing data sets enables the efficient analysis of very large multivariate images. The spectral compression algorithm uses a factored representation of the data that can be obtained from Principal Components Analysis or other factorization technique. Furthermore, a block algorithm can be used for performing common operations more efficiently. An image analysis can be performed on the factored representation of the data, using only the most significant factors. The spectral compression algorithm can be combined with a spatial compression algorithm to provide further computational efficiencies.

  13. Visualization analysis of multivariate spatial-temporal data of the Red Army Long March in China

    Science.gov (United States)

    Ma, Ding; Ma, Zhimin; Meng, Lumin; Li, Xia

    2009-10-01

    Recently, the visualization of spatial-temporal data in historic events is emphasized by more and more people. To provide an efficient and effective approach to meet this requirement is the duty of Geo-data modeling researchers. The aim of the paper is to ground on a new perspective to visualize the multivariate spatial-temporal data of the Red Army Long March, which is one of the most important events of the Chinese modem history. This research focuses on the extraction of relevant information from a 3-dimensional trajectory, which captures object locations in geographic space at specified temporal intervals. However, existing visualization methods cannot deal with the multivariate spatial-temporal data effectively. Thus there is a potential chance to represent and analyze this kind of data in the case study. The thesis combines two visualization methods, the Space-Time-Cube for spatial temporal data and Parallel Coordinates Plots (PCPs) for multivariable data, to develop conceptual GIS database model that facilitates the exploration and analysis of multivariate spatial-temporal data sets in the combination with 3D Space-Time-Path and 2D graphics. The designed model is supported by the geo-visualization environment and integrates diverse sets of multivariate spatial-temporal data and built-up the dynamic process and relationships. It is concluded that this way of geo-visualization can effectively manipulate a large amount of distributed data, realize the high efficient transmission of quantitative and qualitative information and also provide a new research mode in the field of the History of CPC and military affairs.

  14. Sequential Structural and Fluid Dynamics Analysis of Balloon-Expandable Coronary Stents: A Multivariable Statistical Analysis.

    Science.gov (United States)

    Martin, David; Boyle, Fergal

    2015-09-01

    Several clinical studies have identified a strong correlation between neointimal hyperplasia following coronary stent deployment and both stent-induced arterial injury and altered vessel hemodynamics. As such, the sequential structural and fluid dynamics analysis of balloon-expandable stent deployment should provide a comprehensive indication of stent performance. Despite this observation, very few numerical studies of balloon-expandable coronary stents have considered both the mechanical and hemodynamic impact of stent deployment. Furthermore, in the few studies that have considered both phenomena, only a small number of stents have been considered. In this study, a sequential structural and fluid dynamics analysis methodology was employed to compare both the mechanical and hemodynamic impact of six balloon-expandable coronary stents. To investigate the relationship between stent design and performance, several common stent design properties were then identified and the dependence between these properties and both the mechanical and hemodynamic variables of interest was evaluated using statistical measures of correlation. Following the completion of the numerical analyses, stent strut thickness was identified as the only common design property that demonstrated a strong dependence with either the mean equivalent stress predicted in the artery wall or the mean relative residence time predicted on the luminal surface of the artery. These results corroborate the findings of the large-scale ISAR-STEREO clinical studies and highlight the crucial role of strut thickness in coronary stent design. The sequential structural and fluid dynamics analysis methodology and the multivariable statistical treatment of the results described in this study should prove useful in the design of future balloon-expandable coronary stents.

  15. Analysis of the real EADGENE data set: Multivariate approaches and post analysis (Open Access publication

    Directory of Open Access Journals (Sweden)

    Schuberth Hans-Joachim

    2007-11-01

    Full Text Available Abstract The aim of this paper was to describe, and when possible compare, the multivariate methods used by the participants in the EADGENE WP1.4 workshop. The first approach was for class discovery and class prediction using evidence from the data at hand. Several teams used hierarchical clustering (HC or principal component analysis (PCA to identify groups of differentially expressed genes with a similar expression pattern over time points and infective agent (E. coli or S. aureus. The main result from these analyses was that HC and PCA were able to separate tissue samples taken at 24 h following E. coli infection from the other samples. The second approach identified groups of differentially co-expressed genes, by identifying clusters of genes highly correlated when animals were infected with E. coli but not correlated more than expected by chance when the infective pathogen was S. aureus. The third approach looked at differential expression of predefined gene sets. Gene sets were defined based on information retrieved from biological databases such as Gene Ontology. Based on these annotation sources the teams used either the GlobalTest or the Fisher exact test to identify differentially expressed gene sets. The main result from these analyses was that gene sets involved in immune defence responses were differentially expressed.

  16. Enhancing e-waste estimates: Improving data quality by multivariate Input–Output Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Feng, E-mail: fwang@unu.edu [Institute for Sustainability and Peace, United Nations University, Hermann-Ehler-Str. 10, 53113 Bonn (Germany); Design for Sustainability Lab, Faculty of Industrial Design Engineering, Delft University of Technology, Landbergstraat 15, 2628CE Delft (Netherlands); Huisman, Jaco [Institute for Sustainability and Peace, United Nations University, Hermann-Ehler-Str. 10, 53113 Bonn (Germany); Design for Sustainability Lab, Faculty of Industrial Design Engineering, Delft University of Technology, Landbergstraat 15, 2628CE Delft (Netherlands); Stevels, Ab [Design for Sustainability Lab, Faculty of Industrial Design Engineering, Delft University of Technology, Landbergstraat 15, 2628CE Delft (Netherlands); Baldé, Cornelis Peter [Institute for Sustainability and Peace, United Nations University, Hermann-Ehler-Str. 10, 53113 Bonn (Germany); Statistics Netherlands, Henri Faasdreef 312, 2492 JP Den Haag (Netherlands)

    2013-11-15

    Highlights: • A multivariate Input–Output Analysis method for e-waste estimates is proposed. • Applying multivariate analysis to consolidate data can enhance e-waste estimates. • We examine the influence of model selection and data quality on e-waste estimates. • Datasets of all e-waste related variables in a Dutch case study have been provided. • Accurate modeling of time-variant lifespan distributions is critical for estimate. - Abstract: Waste electrical and electronic equipment (or e-waste) is one of the fastest growing waste streams, which encompasses a wide and increasing spectrum of products. Accurate estimation of e-waste generation is difficult, mainly due to lack of high quality data referred to market and socio-economic dynamics. This paper addresses how to enhance e-waste estimates by providing techniques to increase data quality. An advanced, flexible and multivariate Input–Output Analysis (IOA) method is proposed. It links all three pillars in IOA (product sales, stock and lifespan profiles) to construct mathematical relationships between various data points. By applying this method, the data consolidation steps can generate more accurate time-series datasets from available data pool. This can consequently increase the reliability of e-waste estimates compared to the approach without data processing. A case study in the Netherlands is used to apply the advanced IOA model. As a result, for the first time ever, complete datasets of all three variables for estimating all types of e-waste have been obtained. The result of this study also demonstrates significant disparity between various estimation models, arising from the use of data under different conditions. It shows the importance of applying multivariate approach and multiple sources to improve data quality for modelling, specifically using appropriate time-varying lifespan parameters. Following the case study, a roadmap with a procedural guideline is provided to enhance e

  17. Regional magnetic resonance imaging measures for multivariate analysis in Alzheimer's disease and mild cognitive impairment.

    Science.gov (United States)

    Westman, Eric; Aguilar, Carlos; Muehlboeck, J-Sebastian; Simmons, Andrew

    2013-01-01

    Automated structural magnetic resonance imaging (MRI) processing pipelines are gaining popularity for Alzheimer's disease (AD) research. They generate regional volumes, cortical thickness measures and other measures, which can be used as input for multivariate analysis. It is not clear which combination of measures and normalization approach are most useful for AD classification and to predict mild cognitive impairment (MCI) conversion. The current study includes MRI scans from 699 subjects [AD, MCI and controls (CTL)] from the Alzheimer's disease Neuroimaging Initiative (ADNI). The Freesurfer pipeline was used to generate regional volume, cortical thickness, gray matter volume, surface area, mean curvature, gaussian curvature, folding index and curvature index measures. 259 variables were used for orthogonal partial least square to latent structures (OPLS) multivariate analysis. Normalisation approaches were explored and the optimal combination of measures determined. Results indicate that cortical thickness measures should not be normalized, while volumes should probably be normalized by intracranial volume (ICV). Combining regional cortical thickness measures (not normalized) with cortical and subcortical volumes (normalized with ICV) using OPLS gave a prediction accuracy of 91.5 % when distinguishing AD versus CTL. This model prospectively predicted future decline from MCI to AD with 75.9 % of converters correctly classified. Normalization strategy did not have a significant effect on the accuracies of multivariate models containing multiple MRI measures for this large dataset. The appropriate choice of input for multivariate analysis in AD and MCI is of great importance. The results support the use of un-normalised cortical thickness measures and volumes normalised by ICV.

  18. Detecting Neuroimaging Biomarkers for Depression: A Meta-analysis of Multivariate Pattern Recognition Studies.

    Science.gov (United States)

    Kambeitz, Joseph; Cabral, Carlos; Sacchet, Matthew D; Gotlib, Ian H; Zahn, Roland; Serpa, Mauricio H; Walter, Martin; Falkai, Peter; Koutsouleris, Nikolaos

    2017-09-01

    Multiple studies have examined functional and structural brain alteration in patients diagnosed with major depressive disorder (MDD). The introduction of multivariate statistical methods allows investigators to utilize data concerning these brain alterations to generate diagnostic models that accurately differentiate patients with MDD from healthy control subjects (HCs). However, there is substantial heterogeneity in the reported results, the methodological approaches, and the clinical characteristics of participants in these studies. We conducted a meta-analysis of all studies using neuroimaging (volumetric measures derived from T1-weighted images, task-based functional magnetic resonance imaging [MRI], resting-state MRI, or diffusion tensor imaging) in combination with multivariate statistical methods to differentiate patients diagnosed with MDD from HCs. Thirty-three (k = 33) samples including 912 patients with MDD and 894 HCs were included in the meta-analysis. Across all studies, patients with MDD were separated from HCs with 77% sensitivity and 78% specificity. Classification based on resting-state MRI (85% sensitivity, 83% specificity) and on diffusion tensor imaging data (88% sensitivity, 92% specificity) outperformed classifications based on structural MRI (70% sensitivity, 71% specificity) and task-based functional MRI (74% sensitivity, 77% specificity). Our results demonstrate the high representational capacity of multivariate statistical methods to identify neuroimaging-based biomarkers of depression. Future studies are needed to elucidate whether multivariate neuroimaging analysis has the potential to generate clinically useful tools for the differential diagnosis of affective disorders and the prediction of both treatment response and functional outcome. Copyright © 2016 Society of Biological Psychiatry. All rights reserved.

  19. Kaempferol, a potential cytostatic and cure for inflammatory disorders.

    Science.gov (United States)

    Rajendran, Peramaiyan; Rengarajan, Thamaraiselvan; Nandakumar, Natarajan; Palaniswami, Rajendran; Nishigaki, Yutaka; Nishigaki, Ikuo

    2014-10-30

    Kaempferol (3,5,7-trihydroxy-2-(4-hydroxyphenyl)-4H-1-benzopyran-4-one) is a flavonoid found in many edible plants (e.g., tea, broccoli, cabbage, kale, beans, endive, leek, tomato, strawberries, and grapes) and in plants or botanical products commonly used in traditional medicine (e.g., Ginkgo biloba, Tilia spp, Equisetum spp, Moringa oleifera, Sophora japonica and propolis). Its anti-oxidant/anti-inflammatory effects have been demonstrated in various disease models, including those for encephalomyelitis, diabetes, asthma, and carcinogenesis. Moreover, kaempferol act as a scavenger of free radicals and superoxide radicals as well as preserve the activity of various anti-oxidant enzymes such as catalase, glutathione peroxidase, and glutathione-S-transferase. The anticancer effect of this flavonoid is mediated through different modes of action, including anti-proliferation, apoptosis induction, cell-cycle arrest, generation of reactive oxygen species (ROS), and anti-metastasis/anti-angiogenesis activities. In addition, kaempferol was found to exhibit its anticancer activity through the modulation of multiple molecular targets including p53 and STAT3, through the activation of caspases, and through the generation of ROS. The anti-tumor effects of kaempferol have also been investigated in tumor-bearing mice. The combination of kaempferol and conventional chemotherapeutic drugs produces a greater therapeutic effect than the latter, as well as reduces the toxicity of the latter. In this review, we summarize the anti-oxidant/anti-inflammatory and anticancer effects of kaempferol with a focus on its molecular targets and the possible use of this flavonoid for the treatment of inflammatory diseases and cancer.

  20. A review on tomato authenticity: quality control methods in conjunction with multivariate analysis (chemometrics).

    Science.gov (United States)

    Arvanitoyannis, Ioannis S; Vaitsi, Olga B

    2007-01-01

    Authenticity and traceability have been two of the most important issues in the food chain. Authenticity in particular, is closely related with both food quality and safety issues. Vegetables stand for a category of foods heavily affected by adulteration either in terms of geographic origin (national or international level) or production methods (organic or conventional production, fertilizers, pesticides, genetically modified vegetables). This review aims at addressing most of the currently applied methods for ensuring quality control of vegetables; a) instrumental: ion chromatography, high pressure liquid chromatography, atomic absorption spectrophotometry, electronic nose and mass spectroscopy and b) sensory analysis. The results of all the above mentioned methods were analyzed by means of multivariate analysis (principal component analysis, discriminant analysis, cluster analysis, canonical analysis, and factor analysis). All ensuing results and conclusions are summarized in eight comprehensive tables.

  1. Multivariate methods for analysis of environmental reference materials using laser-induced breakdown spectroscopy

    Directory of Open Access Journals (Sweden)

    Shikha Awasthi

    2017-06-01

    Full Text Available Analysis of emission from laser-induced plasma has a unique capability for quantifying the major and minor elements present in any type of samples under optimal analysis conditions. Chemometric techniques are very effective and reliable tools for quantification of multiple components in complex matrices. The feasibility of laser-induced breakdown spectroscopy (LIBS in combination with multivariate analysis was investigated for the analysis of environmental reference materials (RMs. In the present work, different (Certified/Standard Reference Materials of soil and plant origin were analyzed using LIBS and the presence of Al, Ca, Mg, Fe, K, Mn and Si were identified in the LIBS spectra of these materials. Multivariate statistical methods (Partial Least Square Regression and Partial Least Square Discriminant Analysis were employed for quantitative analysis of the constituent elements using the LIBS spectral data. Calibration models were used to predict the concentrations of the different elements of test samples and subsequently, the concentrations were compared with certified concentrations to check the authenticity of models. The non-destructive analytical method namely Instrumental Neutron Activation Analysis (INAA using high flux reactor neutrons and high resolution gamma-ray spectrometry was also used for intercomparison of results of two RMs by LIBS.

  2. Multivariate analysis of prognostic factors for idiopathic sudden sensorineural hearing loss in children.

    Science.gov (United States)

    Chung, Jae Ho; Cho, Seok Hyun; Jeong, Jin Hyeok; Park, Chul Won; Lee, Seung Hwan

    2015-09-01

    To evaluate clinical characteristics and possible associated factors of idiopathic sudden sensorineural hearing loss (ISSNHL) in children using univariate and multivariate analyses. A retrospective case series with comparisons. From January 2007 to December 2013, medical records of 37 pediatric ISSNHL patients were reviewed to assess hearing recovery rate and examine factors associated with prognosis (gender; side of hearing loss; opposite side hearing loss; treatment onset; presence of vertigo, tinnitus, and ear fullness; initial hearing threshold), using univariate and multivariate analysis, and compare them with 276 adult ISSNHL patients. Pediatric patients comprised only 6.6% of pediatric/adult cases of ISSNHL, and those below 10 years old were only 0.7%. The overall recovery rates (complete and partial) of the pediatric and adult patients were 57.4% and 47.2%, respectively. The complete recovery rate of the pediatric group (46.6%) was higher than that of the adult group (30.8%, P = .040). According to multivariate analysis, absence of tinnitus, later onset of treatment, and higher hearing threshold at initial presentation were associated with a poor prognosis in pediatric ISSNHL. The recovery rate of ISSNHL in pediatric patients is higher than in adults, and the presence of tinnitus and earlier treatment onset is associated with favorable outcomes. 4. © 2015 The American Laryngological, Rhinological and Otological Society, Inc.

  3. Multivariate Image Analysis in Gaussian Multi-Scale Space for Defect Detection

    Institute of Scientific and Technical Information of China (English)

    Dong-tai Liang; Wei-yan Deng; Xuan-yin Wang; Yang Zhang

    2009-01-01

    Inspired by the coarse-to-fine visual perception process of human vision system, a new approach based on Gaussian multi-scale space for defect detection of industrial products was proposed. By selecting different scale parameters of the Gaussian kernel, the multi-scale representation of the original image data could be obtained and used to constitute the multi-variate image, in which each channel could represent a perceptual observation of the original image from different scales. The Multivariate Image Analysis (MIA) techniques were used to extract defect features information. The MIA combined Principal Component Analysis (PCA) to obtain the principal component scores of the multivariate test image. The Q-statistic image, derived from the residuals after the extraction of the first principal component score and noise, could be used to efficiently reveal the surface defects with an appropriate threshold value decided by training images. Experimental results show that the proposed method performs better than the gray histogram-based method. It has less sensitivity to the inhomogeneous of illumination, and has more robustness and reliability of defect detection with lower pseudo reject rate.

  4. Borrowing of strength and study weights in multivariate and network meta-analysis.

    Science.gov (United States)

    Jackson, Dan; White, Ian R; Price, Malcolm; Copas, John; Riley, Richard D

    2015-11-06

    Multivariate and network meta-analysis have the potential for the estimated mean of one effect to borrow strength from the data on other effects of interest. The extent of this borrowing of strength is usually assessed informally. We present new mathematical definitions of 'borrowing of strength'. Our main proposal is based on a decomposition of the score statistic, which we show can be interpreted as comparing the precision of estimates from the multivariate and univariate models. Our definition of borrowing of strength therefore emulates the usual informal assessment. We also derive a method for calculating study weights, which we embed into the same framework as our borrowing of strength statistics, so that percentage study weights can accompany the results from multivariate and network meta-analyses as they do in conventional univariate meta-analyses. Our proposals are illustrated using three meta-analyses involving correlated effects for multiple outcomes, multiple risk factor associations and multiple treatments (network meta-analysis). © The Author(s) 2015.

  5. Kaempferol and Chrysin Synergies to Improve Septic Mice Survival.

    Science.gov (United States)

    Harasstani, Omar A; Tham, Chau Ling; Israf, Daud A

    2017-01-06

    Previously, we reported the role of synergy between two flavonoids-namely, chrysin and kaempferol-in inhibiting the secretion of a few major proinflammatory mediators such as tumor necrosis factor-alpha (TNF-α), prostaglandin E₂ (PGE₂), and nitric oxide (NO) from lipopolysaccharide (LPS)-induced RAW 264.7 cells. The present study aims to evaluate the effects of this combination on a murine model of polymicrobial sepsis induced by cecal ligation and puncture (CLP). Severe sepsis was induced in male ICR mice (n = 7) via the CLP procedure. The effects of chrysin and kaempferol combination treatment on septic mice were investigated using a 7-day survival study. The levels of key proinflammatory mediators and markers-such as aspartate aminotransferase (AST), TNF-α, and NO-in the sera samples of the septic mice were determined via ELISA and fluorescence determination at different time point intervals post-CLP challenge. Liver tissue samples from septic mice were harvested to measure myeloperoxidase (MPO) levels using a spectrophotometer. Moreover, intraperitoneal fluid (IPF) bacterial clearance and total leukocyte count were also assessed to detect any antibacterial effects exerted by chrysin and kaempferol, individually and in combination. Kaempferol treatment improved the survival rate of CLP-challenged mice by up to 16%. During this treatment, kaempferol expressed antibacterial, antiapoptotic and antioxidant activities through the attenuation of bacterial forming units, AST and NO levels, and increased polymorphonuclear leukocyte (PMN) count in the IPF. On the other hand, the chrysin treatment significantly reduced serum TNF-α levels. However, it failed to significantly improve the survival rate of the CLP-challenged mice. Subsequently, the kaempferol/chrysin combination treatment significantly improved the overall 7-day survival rate by 2-fold-up to 29%. Kaempferol and chrysin revealed some synergistic effects by acting individually upon multiple

  6. MULTIVARIATE MATHEMATICAL MORPHOLOGY FOR DCE-MRI IMAGE ANALYSIS IN ANGIOGENESIS STUDIES

    Directory of Open Access Journals (Sweden)

    Guillaume Noyel

    2014-05-01

    Full Text Available We propose a new computer aided detection framework for tumours acquired on DCE-MRI (Dynamic Contrast Enhanced Magnetic Resonance Imaging series on small animals. To perform this approach, we consider DCE-MRI series as multivariate images. A full multivariate segmentation method based on dimensionality reduction, noise filtering, supervised classification and stochastic watershed is explained and tested on several data sets. The two main key-points introduced in this paper are noise reduction preserving contours and spatio temporal segmentation by stochastic watershed. Noise reduction is performed in a special way to select factorial axes of Factor Correspondence Analysis in order to preserves contours. Then a spatio-temporal approach based on stochastic watershed is used to segment tumours. The results obtained are in accordance with the diagnosis of the medical doctors.

  7. Multivariate regional frequency analysis: Two new methods to increase the accuracy of measures

    Science.gov (United States)

    Abdi, Amin; Hassanzadeh, Yousef; Talatahari, Siamak; Fakheri-Fard, Ahmad; Mirabbasi, Rasoul; Ouarda, Taha B. M. J.

    2017-09-01

    The accurate detection of discordant sites in a heterogeneous region and the estimation of the regional parameters of a statistical distribution are two important issues in multivariate regional frequency analysis. In this study, two new methods are proposed for increasing the accuracy of the multivariate L-moment approach. The first one, the optimization-based method (OBM) is utilized to estimate the best distribution parameters. The second one is the rank-based method (RBM), which is used in the robust discordancy measure for identifying discordant sites. In order to assess the performance of the proposed approaches on the heterogeneity measure, real and simulated regions of drought characteristics are considered. The results confirm the usefulness of the new methods in comparison with some well-established techniques.

  8. Multivariate reference technique for quantitative analysis of fiber-optic tissue Raman spectroscopy.

    Science.gov (United States)

    Bergholt, Mads Sylvest; Duraipandian, Shiyamala; Zheng, Wei; Huang, Zhiwei

    2013-12-03

    We report a novel method making use of multivariate reference signals of fused silica and sapphire Raman signals generated from a ball-lens fiber-optic Raman probe for quantitative analysis of in vivo tissue Raman measurements in real time. Partial least-squares (PLS) regression modeling is applied to extract the characteristic internal reference Raman signals (e.g., shoulder of the prominent fused silica boson peak (~130 cm(-1)); distinct sapphire ball-lens peaks (380, 417, 646, and 751 cm(-1))) from the ball-lens fiber-optic Raman probe for quantitative analysis of fiber-optic Raman spectroscopy. To evaluate the analytical value of this novel multivariate reference technique, a rapid Raman spectroscopy system coupled with a ball-lens fiber-optic Raman probe is used for in vivo oral tissue Raman measurements (n = 25 subjects) under 785 nm laser excitation powers ranging from 5 to 65 mW. An accurate linear relationship (R(2) = 0.981) with a root-mean-square error of cross validation (RMSECV) of 2.5 mW can be obtained for predicting the laser excitation power changes based on a leave-one-subject-out cross-validation, which is superior to the normal univariate reference method (RMSE = 6.2 mW). A root-mean-square error of prediction (RMSEP) of 2.4 mW (R(2) = 0.985) can also be achieved for laser power prediction in real time when we applied the multivariate method independently on the five new subjects (n = 166 spectra). We further apply the multivariate reference technique for quantitative analysis of gelatin tissue phantoms that gives rise to an RMSEP of ~2.0% (R(2) = 0.998) independent of laser excitation power variations. This work demonstrates that multivariate reference technique can be advantageously used to monitor and correct the variations of laser excitation power and fiber coupling efficiency in situ for standardizing the tissue Raman intensity to realize quantitative analysis of tissue Raman measurements in vivo, which is particularly appealing in

  9. Severe pneumonia in the elderly: a multivariate analysis of risk factors.

    Science.gov (United States)

    Li, Wei; Ding, Cheng; Yin, Shaojun

    2015-01-01

    Pneumonia is the second leading reason for hospitalization of medicare beneficiaries. The mortality rate is high, especially in the elderly. In this study, we aimed to determine the risk factors associated with severe pneumonia in the elderly. Retrospective study was conducted and data of old patients with severe pneumonia were collected. They were divided into two groups: the experiment group (death group) and the control (living group). The general situation, underlying diseases, laboratory tests, types of etiology, imaging analysis and treatment situation of patients were analyzed and compared. Univariate analysis and logistic multivariate regression analysis were used to screen the related and independent risk factors for the diagnosis of severe pneumonia in the elderly. In univariate analysis, there were many factors had statistical significance including chronic kidney disease, electrolyte disturbance, low phosphorus and so on. Result of logistic multivariate regression analysis showed pro-BNP level and serum prealbumin were independent risk factors. In sputum culture, the relevance ratio of acinetobacter baumannii was the highest in gram negative bacteria followed by klebsiella pneumoniae. In gram positive bacteria, the relevance ratio of staphylococcus aureus was the highest. In conclusion, the analysis on risk factors for severe pneumonia has great clinical significance on improving the prognosis.

  10. Spatial compression algorithm for the analysis of very large multivariate images

    Science.gov (United States)

    Keenan, Michael R.

    2008-07-15

    A method for spatially compressing data sets enables the efficient analysis of very large multivariate images. The spatial compression algorithms use a wavelet transformation to map an image into a compressed image containing a smaller number of pixels that retain the original image's information content. Image analysis can then be performed on a compressed data matrix consisting of a reduced number of significant wavelet coefficients. Furthermore, a block algorithm can be used for performing common operations more efficiently. The spatial compression algorithms can be combined with spectral compression algorithms to provide further computational efficiencies.

  11. The MIDAS processor. [Multivariate Interactive Digital Analysis System for multispectral scanner data

    Science.gov (United States)

    Kriegler, F. J.; Gordon, M. F.; Mclaughlin, R. H.; Marshall, R. E.

    1975-01-01

    The MIDAS (Multivariate Interactive Digital Analysis System) processor is a high-speed processor designed to process multispectral scanner data (from Landsat, EOS, aircraft, etc.) quickly and cost-effectively to meet the requirements of users of remote sensor data, especially from very large areas. MIDAS consists of a fast multipipeline preprocessor and classifier, an interactive color display and color printer, and a medium scale computer system for analysis and control. The system is designed to process data having as many as 16 spectral bands per picture element at rates of 200,000 picture elements per second into as many as 17 classes using a maximum likelihood decision rule.

  12. Multivariate genetic analysis of brain structure in an extended twin design

    DEFF Research Database (Denmark)

    Posthuma, D; de Geus, E.J.; Neale, M.C.;

    2000-01-01

    . The analysis is carried out on the raw data and specifies a model for the mean and the covariance structure. Results suggest that cerebellar volume and intracranial space vary with age and sex. Brain volumes tend to decrease slightly with age, and males generally have a larger brain volume than females....... Intermediate phenotypes for discrete traits, such as psychiatric disorders, can be neurotransmitter levels, brain function, or structure. In this paper we conduct a multivariate analysis of data from 111 twin pairs and 34 additional siblings on cerebellar volume, intracranial space, and body height...

  13. Assessment of trace elements levels in patients with Type 2 diabetes using multivariate statistical analysis.

    Science.gov (United States)

    Badran, M; Morsy, R; Soliman, H; Elnimr, T

    2016-01-01

    The trace elements metabolism has been reported to possess specific roles in the pathogenesis and progress of diabetes mellitus. Due to the continuous increase in the population of patients with Type 2 diabetes (T2D), this study aims to assess the levels and inter-relationships of fast blood glucose (FBG) and serum trace elements in Type 2 diabetic patients. This study was conducted on 40 Egyptian Type 2 diabetic patients and 36 healthy volunteers (Hospital of Tanta University, Tanta, Egypt). The blood serum was digested and then used to determine the levels of 24 trace elements using an inductive coupled plasma mass spectroscopy (ICP-MS). Multivariate statistical analysis depended on correlation coefficient, cluster analysis (CA) and principal component analysis (PCA), were used to analysis the data. The results exhibited significant changes in FBG and eight of trace elements, Zn, Cu, Se, Fe, Mn, Cr, Mg, and As, levels in the blood serum of Type 2 diabetic patients relative to those of healthy controls. The statistical analyses using multivariate statistical techniques were obvious in the reduction of the experimental variables, and grouping the trace elements in patients into three clusters. The application of PCA revealed a distinct difference in associations of trace elements and their clustering patterns in control and patients group in particular for Mg, Fe, Cu, and Zn that appeared to be the most crucial factors which related with Type 2 diabetes. Therefore, on the basis of this study, the contributors of trace elements content in Type 2 diabetic patients can be determine and specify with correlation relationship and multivariate statistical analysis, which confirm that the alteration of some essential trace metals may play a role in the development of diabetes mellitus. Copyright © 2015 Elsevier GmbH. All rights reserved.

  14. Characterizing the moisture content of tea with diffuse reflectance spectroscopy using wavelet transform and multivariate analysis.

    Science.gov (United States)

    Li, Xiaoli; Xie, Chuanqi; He, Yong; Qiu, Zhengjun; Zhang, Yanchao

    2012-01-01

    Effects of the moisture content (MC) of tea on diffuse reflectance spectroscopy were investigated by integrated wavelet transform and multivariate analysis. A total of 738 representative samples, including fresh tea leaves, manufactured tea and partially processed tea were collected for spectral measurement in the 325-1,075 nm range with a field portable spectroradiometer. Then wavelet transform (WT) and multivariate analysis were adopted for quantitative determination of the relationship between MC and spectral data. Three feature extraction methods including WT, principal component analysis (PCA) and kernel principal component analysis (KPCA) were used to explore the internal structure of spectral data. Comparison of those three methods indicated that the variables generated by WT could efficiently discover structural information of spectral data. Calibration involving seeking the relationship between MC and spectral data was executed by using regression analysis, including partial least squares regression, multiple linear regression and least square support vector machine. Results showed that there was a significant correlation between MC and spectral data (r = 0.991, RMSEP = 0.034). Moreover, the effective wavelengths for MC measurement were detected at range of 888-1,007 nm by wavelet transform. The results indicated that the diffuse reflectance spectroscopy of tea is highly correlated with MC.

  15. Multivariate analysis of the chemical properties of the eroded brown soils

    Directory of Open Access Journals (Sweden)

    Juan Alejandro Villazón Gómez

    2017-01-01

    Full Text Available The work was carried out with the data obtained of 30 profiles of Brown soils classified according to the effect of erosion. With the objective of determining, by means of a multivariate analysis, the effect of the erosion on the chemicals properties of the Brown soils was carried out a Discriminant and Principals Components Analysis. It was evaluated the chemicals variables pH in water, pH in KCl, organic matter, calcium, magnesium, potassium, sodium and S, T and V values. The Multivariate Analysis allowed establishing that magnesium is the only chemical property that evidence contraposition with the other variables, due to the harmful effect that this base exerts on the soil aggregates, which can accelerate or stressing the action of the erosive processes in the Brown soils. In the Principals Components Analysis, then components represented by the influence of the soil reaction, the absorbing complex and magnesium accumulate 78.75 % of the variance. The Discriminant Analysis explains the 97.06 % of the total of the variation in the two first axes, with the 93.33 % of good classification, with all the groups conformed by the categories of erosion well told apart among themselves.

  16. Characterizing the Moisture Content of Tea with Diffuse Reflectance Spectroscopy Using Wavelet Transform and Multivariate Analysis

    Directory of Open Access Journals (Sweden)

    Chuanqi Xie

    2012-07-01

    Full Text Available Effects of the moisture content (MC of tea on diffuse reflectance spectroscopy were investigated by integrated wavelet transform and multivariate analysis. A total of 738 representative samples, including fresh tea leaves, manufactured tea and partially processed tea were collected for spectral measurement in the 325–1,075 nm range with a field portable spectroradiometer. Then wavelet transform (WT and multivariate analysis were adopted for quantitative determination of the relationship between MC and spectral data. Three feature extraction methods including WT, principal component analysis (PCA and kernel principal component analysis (KPCA were used to explore the internal structure of spectral data. Comparison of those three methods indicated that the variables generated by WT could efficiently discover structural information of spectral data. Calibration involving seeking the relationship between MC and spectral data was executed by using regression analysis, including partial least squares regression, multiple linear regression and least square support vector machine. Results showed that there was a significant correlation between MC and spectral data (r = 0.991, RMSEP = 0.034. Moreover, the effective wavelengths for MC measurement were detected at range of 888–1,007 nm by wavelet transform. The results indicated that the diffuse reflectance spectroscopy of tea is highly correlated with MC.

  17. Romanian Wines Quality and Authenticity Using FT-MIR Spectroscopy Coupled with Multivariate Data Analysis

    Directory of Open Access Journals (Sweden)

    Roxana BANC

    2014-12-01

    Full Text Available Fourier Transform Mid-Infrared Spectroscopy (FT-MIR combined with multivariate data analysis have been applied for the discrimination of 15 different Romanian wines (white, rosé and red wines, obtained from different origin-denominated cultivars. Principal component analysis and hierarchical cluster analysis was performed using different regions of FT-MIR spectra for all wines. The general fingerprint of wines was splitted in four characteristic regions, corresponding to phenolic derivatives, carbohydrates, amino acids and organic acids, which confer the wines quality and authenticity. By qualitative and quantitative evaluation of each component category, it was possible to discriminate each wine category, from red, to rosé and white colours, to dry, half-dry and half-sweet flavours. The multivariate data analysis based on absorption peaks from FT-MIR spectra demonstrated a very good, significant clustering of samples, based on the four main components: phenolics, carbohydrates, amino acids and organic acids. Therefore, the ATR-FT-MIR analysis proved to be a very fast, cheap and efficient tool to evaluate the quality and authenticity of wines, and to discriminate each wine category, based on their colour and sweetness, as consequence of their biological (cultivar specificity.

  18. Identification of regioisomers of methylated kaempferol and quercetin by ultra high performance liquid chromatography quadrupole time-of-flight (UHPLC–QTOF) tandem mass spectrometry combined with diagnostic fragmentation pattern analysis

    Energy Technology Data Exchange (ETDEWEB)

    Ma, Chengying; Lv, Haipeng; Zhang, Xinzhong; Chen, Zongmao; Shi, Jiang [Tea Research Institute, Chinese Academy of Agricultural Sciences, 9 Meiling South Road, Hangzhou, Zhejiang 310008 (China); Lu, Meiling, E-mail: meilinglu@hotmail.com [Chemical Analysis Group, Agilent Technologies, No. 3 Wangjing North Road, Chaoyang Distr., Beijing 100102 (China); Lin, Zhi, E-mail: linz@mail.tricaas.com [Tea Research Institute, Chinese Academy of Agricultural Sciences, 9 Meiling South Road, Hangzhou, Zhejiang 310008 (China)

    2013-09-17

    Highlights: •Found methane elimination is position-specific for methylated flavonols. •Found retro Diels–Alder fragments retained methoxy at original ring of flavonols. •Proposed a diagnostic pattern for discriminating regioisomers of flavonols. •Identified the specificity of three novel flavonol O-methyltransferases. •Identified six biologically active compounds and four new compounds. -- Abstract: The O-methylation of active flavonoids can enhance their antiallergic, anticancerous, and cardioprotective effects depending on the methylation position. Thus, it is biologically and pharmacologically important to differentiate methylated flavonoid regioisomers. In this study, we examined the regioisomers of methylated kaempferol and quercetin using ultra high performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry. The methyl groups on the flavonoids can generally be cleaved as methyl radicals in a position-independent manner. We found that methyl groups can be cleaved as methane. If there are protons adjacent the methoxy on the flavonol rings, intra-molecule proton transfer can occur via collision-induced dissociation, and one molecule of methane can then be eliminated. The remaining charged fragment ([M+H−CH{sub 4}]{sup +}) reflects the adjacent structure and is specific to the methoxy position. Furthermore, the retro Diels–Alder (RDA) fragmentation of methylated flavonols can generate fragments with the methoxy at the original methylated ring. Combining the position-specific [M+H−CH{sub 4}]{sup +} fragment with the RDA fragments provides a diagnostic pattern for rapidly identifying methylated regioisomeric flavonols. Along with their retention behaviour, we have successfully identified ten regioisomers of methylated kaempferol and quercetin, which include six compounds previously reported in plants and shown to be biologically active. The developed approach is sensitive, rapid, reliable, and requires few standard

  19. Combination of multivariate curve resolution and multivariate classification techniques for comprehensive high-performance liquid chromatography-diode array absorbance detection fingerprints analysis of Salvia reuterana extracts.

    Science.gov (United States)

    Hakimzadeh, Neda; Parastar, Hadi; Fattahi, Mohammad

    2014-01-24

    In this study, multivariate curve resolution (MCR) and multivariate classification methods are proposed to develop a new chemometric strategy for comprehensive analysis of high-performance liquid chromatography-diode array absorbance detection (HPLC-DAD) fingerprints of sixty Salvia reuterana samples from five different geographical regions. Different chromatographic problems occurred during HPLC-DAD analysis of S. reuterana samples, such as baseline/background contribution and noise, low signal-to-noise ratio (S/N), asymmetric peaks, elution time shifts, and peak overlap are handled using the proposed strategy. In this way, chromatographic fingerprints of sixty samples are properly segmented to ten common chromatographic regions using local rank analysis and then, the corresponding segments are column-wise augmented for subsequent MCR analysis. Extended multivariate curve resolution-alternating least squares (MCR-ALS) is used to obtain pure component profiles in each segment. In general, thirty-one chemical components were resolved using MCR-ALS in sixty S. reuterana samples and the lack of fit (LOF) values of MCR-ALS models were below 10.0% in all cases. Pure spectral profiles are considered for identification of chemical components by comparing their resolved spectra with the standard ones and twenty-four components out of thirty-one components were identified. Additionally, pure elution profiles are used to obtain relative concentrations of chemical components in different samples for multivariate classification analysis by principal component analysis (PCA) and k-nearest neighbors (kNN). Inspection of the PCA score plot (explaining 76.1% of variance accounted for three PCs) showed that S. reuterana samples belong to four clusters. The degree of class separation (DCS) which quantifies the distance separating clusters in relation to the scatter within each cluster is calculated for four clusters and it was in the range of 1.6-5.8. These results are then

  20. Refined composite multivariate generalized multiscale fuzzy entropy: A tool for complexity analysis of multichannel signals

    Science.gov (United States)

    Azami, Hamed; Escudero, Javier

    2017-01-01

    Multiscale entropy (MSE) is an appealing tool to characterize the complexity of time series over multiple temporal scales. Recent developments in the field have tried to extend the MSE technique in different ways. Building on these trends, we propose the so-called refined composite multivariate multiscale fuzzy entropy (RCmvMFE) whose coarse-graining step uses variance (RCmvMFEσ2) or mean (RCmvMFEμ). We investigate the behavior of these multivariate methods on multichannel white Gaussian and 1/ f noise signals, and two publicly available biomedical recordings. Our simulations demonstrate that RCmvMFEσ2 and RCmvMFEμ lead to more stable results and are less sensitive to the signals' length in comparison with the other existing multivariate multiscale entropy-based methods. The classification results also show that using both the variance and mean in the coarse-graining step offers complexity profiles with complementary information for biomedical signal analysis. We also made freely available all the Matlab codes used in this paper.

  1. Sensitivity Analysis for the Decomposition of Mixed Partitioned Multivariate Models into Two Seemingly Unrelated Submodels

    Directory of Open Access Journals (Sweden)

    Eva Fišerová

    2014-06-01

    Full Text Available The paper is focused on the decomposition of mixed partitioned multivariate models into two seemingly unrelated submodels in order to obtain more efficient estimators. The multiresponses are independently normally distributed with the same covariance matrix. The partitioned multivariate model is considered either with, or without an intercept. The elimination transformation of the intercept that preserves the BLUEs of parameter matri- ces and the MINQUE of the variance components in multivariate models with and without an intercept is stated. Procedures on testing the decomposition of the partitioned model are presented. The properties of plug-in test statistics as functions of variance compo- nents are investigated by sensitivity analysis and insensitivity regions for the significance level are proposed. The insensitivity region is a safe region in the parameter space of the variance components where the approximation of the variance components can be used without any essential deterioration of the significance level of the plug-in test statistic. The behavior of plug-in test statistics and insensitivity regions is studied by simulations. 

  2. Multivariate and 2D Extensions of Singular Spectrum Analysis with the Rssa Package

    Directory of Open Access Journals (Sweden)

    Nina Golyandina

    2015-10-01

    Full Text Available Implementation of multivariate and 2D extensions of singular spectrum analysis (SSA by means of the R package Rssa is considered. The extensions include MSSA for simultaneous analysis and forecasting of several time series and 2D-SSA for analysis of digital images. A new extension of 2D-SSA analysis called shaped 2D-SSA is introduced for analysis of images of arbitrary shape, not necessary rectangular. It is shown that implementation of shaped 2D-SSA can serve as a basis for implementation of MSSA and other generalizations. Efficient implementation of operations with Hankel and Hankel-block-Hankel matrices through the fast Fourier transform is suggested. Examples with code fragments in R, which explain the methodology and demonstrate the proper use of Rssa, are presented.

  3. Multivariate Classification of Original and Fake Perfumes by Ion Analysis and Ethanol Content.

    Science.gov (United States)

    Gomes, Clêrton L; de Lima, Ari Clecius A; Loiola, Adonay R; da Silva, Abel B R; Cândido, Manuela C L; Nascimento, Ronaldo F

    2016-07-01

    The increased marketing of fake perfumes has encouraged us to investigate how to identify such products by their chemical characteristics and multivariate analysis. The aim of this study was to present an alternative approach to distinguish original from fake perfumes by means of the investigation of sodium, potassium, chloride ions, and ethanol contents by chemometric tools. For this, 50 perfumes were used (25 original and 25 counterfeit) for the analysis of ions (ion chromatography) and ethanol (gas chromatography). The results demonstrated that the fake perfume had low levels of ethanol and high levels of chloride compared to the original product. The data were treated by chemometric tools such as principal component analysis and linear discriminant analysis. This study proved that the analysis of ethanol is an effective method of distinguishing original from the fake products, and it may potentially be used to assist legal authorities in such cases. © 2016 American Academy of Forensic Sciences.

  4. Integrated environmental monitoring and multivariate data analysis-A case study.

    Science.gov (United States)

    Eide, Ingvar; Westad, Frank; Nilssen, Ingunn; de Freitas, Felipe Sales; Dos Santos, Natalia Gomes; Dos Santos, Francisco; Cabral, Marcelo Montenegro; Bicego, Marcia Caruso; Figueira, Rubens; Johnsen, Ståle

    2017-03-01

    The present article describes integration of environmental monitoring and discharge data and interpretation using multivariate statistics, principal component analysis (PCA), and partial least squares (PLS) regression. The monitoring was carried out at the Peregrino oil field off the coast of Brazil. One sensor platform and 3 sediment traps were placed on the seabed. The sensors measured current speed and direction, turbidity, temperature, and conductivity. The sediment trap samples were used to determine suspended particulate matter that was characterized with respect to a number of chemical parameters (26 alkanes, 16 PAHs, N, C, calcium carbonate, and Ba). Data on discharges of drill cuttings and water-based drilling fluid were provided on a daily basis. The monitoring was carried out during 7 campaigns from June 2010 to October 2012, each lasting 2 to 3 months due to the capacity of the sediment traps. The data from the campaigns were preprocessed, combined, and interpreted using multivariate statistics. No systematic difference could be observed between campaigns or traps despite the fact that the first campaign was carried out before drilling, and 1 of 3 sediment traps was located in an area not expected to be influenced by the discharges. There was a strong covariation between suspended particulate matter and total N and organic C suggesting that the majority of the sediment samples had a natural and biogenic origin. Furthermore, the multivariate regression showed no correlation between discharges of drill cuttings and sediment trap or turbidity data taking current speed and direction into consideration. Because of this lack of correlation with discharges from the drilling location, a more detailed evaluation of chemical indicators providing information about origin was carried out in addition to numerical modeling of dispersion and deposition. The chemical indicators and the modeling of dispersion and deposition support the conclusions from the multivariate

  5. Risk factors for baclofen pump infection in children: a multivariate analysis.

    Science.gov (United States)

    Spader, Heather S; Bollo, Robert J; Bowers, Christian A; Riva-Cambrin, Jay

    2016-06-01

    OBJECTIVE Intrathecal baclofen infusion systems to manage severe spasticity and dystonia are associated with higher infection rates in children than in adults. Factors unique to this population, such as poor nutrition and physical limitations for pump placement, have been hypothesized as the reasons for this disparity. The authors assessed potential risk factors for infection in a multivariate analysis. METHODS Patients who underwent implantation of a programmable pump and intrathecal catheter for baclofen infusion at a single center between January 1, 2000, and March 1, 2012, were identified in this retrospective cohort study. The primary end point was infection. Potential risk factors investigated included preoperative (i.e., demographics, body mass index [BMI], gastrostomy tube, tracheostomy, previous spinal fusion), intraoperative (i.e., surgeon, antibiotics, pump size, catheter location), and postoperative (i.e., wound dehiscence, CSF leak, and number of revisions) factors. Univariate analysis was performed, and a multivariate logistic regression model was created to identify independent risk factors for infection. RESULTS A total of 254 patients were evaluated. The overall infection rate was 9.8%. Univariate analysis identified young age, shorter height, lower weight, dehiscence, CSF leak, and number of revisions within 6 months of pump placement as significantly associated with infection. Multivariate analysis identified young age, dehiscence, and number of revisions as independent risk factors for infection. CONCLUSIONS Young age, wound dehiscence, and number of revisions were independent risk factors for infection in this pediatric cohort. A low BMI and the presence of either a gastrostomy or tracheostomy were not associated with infection and may not be contraindications for this procedure.

  6. Multivariate Statistical Modelling of Compound Events via Pair-Copula Constructions: Analysis of Floods in Ravenna

    Science.gov (United States)

    Bevacqua, Emanuele; Maraun, Douglas; Hobæk Haff, Ingrid; Widmann, Martin; Vrac, Mathieu

    2017-04-01

    Compound events are multivariate extreme events in which the individual contributing variables may not be extreme themselves, but their joint - dependent - occurrence causes an extreme impact. The conventional univariate statistical analysis cannot give accurate information regarding the multivariate nature of these events. We develop a conceptual model, implemented via pair-copula constructions, which allows for the quantification of the risk associated with compound events in present day and future climate, as well as the uncertainty estimates around such risk. The model includes meteorological predictors which provide insight into both the involved physical processes, and the temporal variability of CEs. Moreover, this model provides multivariate statistical downscaling of compound events. Downscaling of compound events is required to extend their risk assessment to the past or future climate, where climate models either do not simulate realistic values of the local variables driving the events, or do not simulate them at all. Based on the developed model, we study compound floods, i.e. joint storm surge and high river runoff, in Ravenna (Italy). To explicitly quantify the risk, we define the impact of compound floods as a function of sea and river levels. We use meteorological predictors to extend the analysis to the past, and get a more robust risk analysis. We quantify the uncertainties of the risk analysis observing that they are very large due to the shortness of the available data, though this may also be the case in other studies where they have not been estimated. Ignoring the dependence between sea and river levels would result in an underestimation of risk, in particular the expected return period of the highest compound flood observed increases from about 20 to 32 years when switching from the dependent to the independent case.

  7. Multivariate meta-analysis of proteomics data from human prostate and colon tumours

    Directory of Open Access Journals (Sweden)

    Lehtiö Janne

    2010-09-01

    Full Text Available Abstract Background There is a vast need to find clinically applicable protein biomarkers as support in cancer diagnosis and tumour classification. In proteomics research, a number of methods can be used to obtain systemic information on protein and pathway level on cells and tissues. One fundamental tool in analysing protein expression has been two-dimensional gel electrophoresis (2DE. Several cancer 2DE studies have reported partially redundant lists of differently expressed proteins. To be able to further extract valuable information from existing 2DE data, the power of a multivariate meta-analysis will be evaluated in this work. Results We here demonstrate a multivariate meta-analysis of 2DE proteomics data from human prostate and colon tumours. We developed a bioinformatic workflow for identifying common patterns over two tumour types. This included dealing with pre-processing of data and handling of missing values followed by the development of a multivariate Partial Least Squares (PLS model for prediction and variable selection. The variable selection was based on the variables performance in the PLS model in combination with stability in the validation. The PLS model development and variable selection was rigorously evaluated using a double cross-validation scheme. The most stable variables from a bootstrap validation gave a mean prediction success of 93% when predicting left out test sets on models discriminating between normal and tumour tissue, common for the two tumour types. The analysis conducted in this study identified 14 proteins with a common trend between the tumour types prostate and colon, i.e. the same expression profile between normal and tumour samples. Conclusions The workflow for meta-analysis developed in this study enabled the finding of a common protein profile for two malign tumour types, which was not possible to identify when analysing the data sets separately.

  8. Risk Factors for Medical Complication after Cervical Spine Surgery: a multivariate analysis of 582 patients

    Science.gov (United States)

    Lee, Michael J.; Konodi, Mark A.; Cizik, Amy M.; Weinreich, Mark A.; Bransford, Richard J.; Bellabarba, Carlo; Chapman, Jens

    2012-01-01

    Study Design Multivariate analysis of prospectively collected registry data Objective Using multivariate analysis, to determine significant risk factors for medical complication after cervical spine surgery. Summary of Background Data Several studies have examined the occurrence of medical complication after spine surgery. However many of these studies have been done utilizing large national databases. While these allow for analysis of thousands of patients, potentially influential co-variates are not accounted for in these retrospective studies. Furthermore, the accuracy of these retrospective data collection in these databases has been called into question. Methods The Spine End Results Registry (2003–2004) is a collection prospectively collected data on all patients who underwent spine surgery at our two institutions. Extensive demographic and medical information were prospectively recorded as described previously by Mirza et al. Complications were defined in detail a priori and were prospectively recorded for at least 2 years after surgery. We analyzed risk factors for medical complication after lumbar spine surgery using univariate and multivariate analysis. Results We analayzed data from 582 patients who met out inclusion criteria. The cumulative incidences of complication after cervical spine surgery per organ system are as follows: cardiac – 8.4%, pulmonary – 13%, gastrointestinal – 3.9%, neurological – 7.4%, hematological – 10.8% and urologic complications – 9.2%. The occurrence of cardiac or respiratory complication after cervical spine surgery was significantly associated with death within 2 years (RR 4.32, 6.43 respectively). Relative risk values with 95% confidence intervals and p values are listed individually in Tables 2 and 3. Conclusion Risk factors identified in this study can be beneficial to clinicians and patients alike when considering surgical treatment of the cervical spine. Future analyses and models that predict the

  9. Groundwater quality in Imphal West district, Manipur, India, with multivariate statistical analysis of data.

    Science.gov (United States)

    Singh, Elangbam J K; Gupta, Abhik; Singh, N R

    2013-04-01

    The aim of this paper was to analyze the groundwater quality of Imphal West district, Manipur, India, and assess its suitability for drinking, domestic, and agricultural use. Eighteen physico-chemical variables were analyzed in groundwater from 30 different hand-operated tube wells in urban, suburban, and rural areas in two seasons. The data were subjected to uni-, bi-, and multivariate statistical analysis, the latter comprising cluster analysis (CA), principal component analysis (PCA), and factor analysis (FA). Arsenic concentrations exceed the Indian standard in 23.3% and the WHO limit in 73.3% of the groundwater sources with only 26.7% in the acceptable range. Several variables like iron, chloride, sodium, sulfate, total dissolved solids, and turbidity are also beyond their desirable limits for drinking water in a number of sites. Sodium concentrations and sodium absorption ratio (SAR) are both high to render the water from the majority of the sources unsuitable for agricultural use. Multivariate statistical techniques, especially varimax rotation of PCA data helped to bring to focus the hidden yet important variables and understand their roles in influencing groundwater quality. Widespread arsenic contamination and high sodium concentration of groundwater pose formidable constraints towards its exploitation for drinking and other domestic and agricultural use in the study area, although urban anthropogenic impacts are not yet pronounced.

  10. PyMVPA: A Python toolbox for multivariate pattern analysis of fMRI data

    Science.gov (United States)

    Hanke, Michael; Halchenko, Yaroslav O.; Sederberg, Per B.; Hanson, Stephen José; Haxby, James V.; Pollmann, Stefan

    2009-01-01

    Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. Here we introduce a Python-based, cross-platform, and open-source software toolbox, called PyMVPA, for the application of classifier-based analysis techniques to fMRI datasets. PyMVPA makes use of Python's ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine-learning packages. We present the framework in this paper and provide illustrative examples on its usage, features, and programmability. PMID:19184561

  11. Risk Factors for Hypertension After Living Donor Kidney Transplantation in Korea: A Multivariate Analysis.

    Science.gov (United States)

    Yu, H; Kim, H S; Baek, C H; Shin, E H; Cho, H J; Han, D J; Park, S K

    2016-01-01

    Post-transplantation hypertension is very common and is associated with cardiovascular complications and poor graft survival in kidney transplant recipients. This study aimed to identify risk factors for hypertension after living donor kidney transplantation. We retrospectively analyzed patients who underwent renal transplantation between January 2009 and April 2012. Hypertension was defined as the use of antihypertensive medications at 12 months post-transplantation. Student t test and chi-squared test were performed for univariate analysis. Logistic regression analysis was performed for multivariate analysis. Five-hundred thirty-nine patients were enrolled in the analyses. The rate of antihypertensive medication use was 67% at 12 months. In multivariate analysis, male gender (odds ratio [OR], 2.68; 95% confidence interval [CI], 1.55-4.61), pretransplantation hypertension (OR, 4.65; 95% CI, 2.14-10.11), donor hypertension (OR, 3.23; 95% CI, 1.05-9.96), high body mass index (BMI; OR, 1.21; 95% CI, 1.12-1.29), and use of cyclosporine (OR, 2.05; 95% CI, 1.28-3.27) were associated with post-transplantation hypertension. These data show that male recipient, hypertension before transplantation, donor hypertension, high BMI, and cyclosporine use were independent factors associated with hypertension. It would be useful to predict and prevention the hypertension after kidney transplantation. Copyright © 2016 Elsevier Inc. All rights reserved.

  12. PyMVPA: A python toolbox for multivariate pattern analysis of fMRI data.

    Science.gov (United States)

    Hanke, Michael; Halchenko, Yaroslav O; Sederberg, Per B; Hanson, Stephen José; Haxby, James V; Pollmann, Stefan

    2009-01-01

    Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. Here we introduce a Python-based, cross-platform, and open-source software toolbox, called PyMVPA, for the application of classifier-based analysis techniques to fMRI datasets. PyMVPA makes use of Python's ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine learning packages. We present the framework in this paper and provide illustrative examples on its usage, features, and programmability.

  13. Joint analysis of multiple blood pressure phenotypes in GAW19 data by using a multivariate rare-variant association test.

    Science.gov (United States)

    Sun, Jianping; Bhatnagar, Sahir R; Oualkacha, Karim; Ciampi, Antonio; Greenwood, Celia M T

    2016-01-01

    Large-scale sequencing studies often measure many related phenotypes in addition to the genetic variants. Joint analysis of multiple phenotypes in genetic association studies may increase power to detect disease-associated loci. We apply a recently developed multivariate rare-variant association test to the Genetic Analysis Workshop 19 data in order to test associations between genetic variants and multiple blood pressure phenotypes simultaneously. We also compare this multivariate test with a widely used univariate test that analyzes phenotypes separately. The multivariate test identified 2 genetic variants that have been previously reported as associated with hypertension or coronary artery disease. In addition, our region-based analyses also show that the multivariate test tends to give smaller p values than the univariate test. Hence, the multivariate test has potential to improve test power, especially when multiple phenotypes are correlated.

  14. Multivariate analysis of risk factors for postoperative complications after laparoscopic liver resection.

    Science.gov (United States)

    Tranchart, Hadrien; Gaillard, Martin; Chirica, Mircea; Ferretti, Stefano; Perlemuter, Gabriel; Naveau, Sylvie; Dagher, Ibrahim

    2015-09-01

    The identification of modifiable perioperative risk factors in patients undergoing laparoscopic liver resection (LLR) should aid the selection of appropriate surgical procedures and thus improve further the outcomes associated with LLR. The aim of this retrospective study was to determine the risk factors for postoperative morbidity associated with laparoscopic liver surgery. All patients who underwent elective LLR between January 1999 and December 2012 were included. Demographic data, preoperative risk factors, operative variables, histological analysis, and postoperative course were recorded. Multivariate analysis was carried out using an unconditional logistic regression model. Between January 1999 and December 2012, 140 patients underwent LLR. There were 56 male patients (40%) and mean age was 57.8 ± 17 years. Postoperative complications were recorded in 30 patients (21.4%). Postoperative morbidity was significantly higher after LLR of malignant tumors [n = 26 (41.3%)] when compared to LLR of benign lesions [n = 4 (5.2%) (P multivariate analysis, operative time [OR = 1.008 (1.003-1.01), P = 0.001] and LLR performed for malignancy [OR = 9.8 (2.5-37.6); P = 0.01] were independent predictors of postoperative morbidity. In the subgroup of patients that underwent LLR for malignancy using the same multivariate model, operative time was the sole independent predictor of postoperative morbidity [OR = 1.008 (1.002-1.013); P = 0.004]. Postoperative complication rate increases by 60% with each additional operative hour during LLR. Therefore, expected operative time should be assessed before and during LLR, especially when dealing with malignant tumor.

  15. Characterisation of DNA methylation status using spectroscopy (mid-IR versus Raman) with multivariate analysis.

    Science.gov (United States)

    Kelly, Jemma G; Najand, Ghazal M; Martin, Francis L

    2011-05-01

    Methylation status plays important roles in the regulation of gene expression and significantly influences the dynamics, bending and flexibility of DNA. The aim of this study was to determine whether attenuated total reflection Fourier-transform infrared (ATR-FTIR) or Raman spectroscopy with subsequent multivariate analysis could determine methylation patterning in oligonucleotides variously containing 5-methylcytosine, cytosine and guanine bases. Applied to Low-E reflective glass slides, 10 independent spectral acquisitions were acquired per oligonucleotide sample. Resultant spectra were baseline-corrected and vector normalised over the 1750 cm(-1) -760 cm(-1) (for ATR-FTIR spectroscopy) or the 1750 cm(-1) -600 cm(-1) (for Raman spectroscopy) regions. Data were then analysed using principal component analysis (PCA) coupled with linear discriminant analysis (LDA). Exploiting this approach, biomolecular signatures enabling sensitive and specific discrimination of methylation patterning were derived. For DNA sequence and methylation analysis, this approach has the potential to be an important tool, especially when material is scarce.

  16. Multivariate statistical analysis for the surface water quality of the Luan River, China

    Institute of Scientific and Technical Information of China (English)

    Zhi-wei ZHAO; Fu-yi CUI

    2009-01-01

    In order to analyze the characteristics of surface water resource quality for the reconstruction of old water treatment plant, multivariate statistical techniques such as cluster analysis and factor analysis were applied to the data of Yuqiao Reservoir--surface water resource of the Luan River, China. The results of cluster analysis demonstrate that the months of one year were divided into 3 groups and the characteristic of clusters was agreed with the seasonal characteristics in North China. Three factors were derived from the complicated set using factor analysis. Factor 1 included turbidity and chlorophyll, which seemed to be related to the anthropogenic activities; factor 2 included alkaline and hardness, which were related to the natural characteristic of surface water; and factor 3 included Cl and NO-N affected by mineral and agricultural activities. The sinusoidal shape of the score plots of the three factors shows that the temporal variations caused by natural and human factors are linked to seasouality.

  17. Multivariate data analysis as a fast tool in evaluation of solid state phenomena

    DEFF Research Database (Denmark)

    Jørgensen, Anna Cecilia; Miroshnyk, Inna; Karjalainen, Milja

    2006-01-01

    A thorough understanding of solid state properties is of growing importance. It is often necessary to apply multiple techniques offering complementary information to fully understand the solid state behavior of a given compound and the relations between various polymorphic forms. The vast amount...... of information generated can be overwhelming and the need for more effective data analysis tools is well recognized. The aim of this study was to investigate the use of multivariate data analysis, in particular principal component analysis (PCA), for fast analysis of solid state information. The data sets...... or wavenumbers) that changed could be identified by the careful interpretation of the loadings plots. The PCA approach provides an effective tool for fast screening of solid state information....

  18. Multivariate analysis of stress in experimental ecosystems by principal response curves and similarity analysis

    NARCIS (Netherlands)

    Brink, van den P.J.; Braak, ter C.J.F.

    1998-01-01

    Experiments in microcosms and mesocosms, which can be carried out in an advanced tier of risk assessment, usually result in large data sets on the dynamics of biological communities of treated and control cosms. Multivariate techniques are an accepted tool to evaluate the community treatment effects

  19. Asymptotics on Semiparametric Analysis of Multivariate Failure Time Data Under the Additive Hazards Model

    Institute of Scientific and Technical Information of China (English)

    Huan-bin Liu; Liu-quan Sun; Li-xing Zhu

    2005-01-01

    Many survival studies record the times to two or more distinct failures on each subject. The failures may be events of different natures or may be repetitions of the same kind of event. In this article, we consider the regression analysis of such multivariate failure time data under the additive hazards model. Simple weighted estimating functions for the regression parameters are proposed, and asymptotic distribution theory of the resulting estimators are derived. In addition, a class of generalized Wald and generalized score statistics for hypothesis testing and model selection are presented, and the asymptotic properties of these statistics are examined.

  20. Moors and Christians: an example of multivariate analysis applied to human blood-groups.

    Science.gov (United States)

    Reyment, R A

    1983-01-01

    Published data on the frequencies of the alleles of the ABO, MNS, and Rh systems for populations in the western Mediterranean region are analysed by the multivariate statistical methods of canonical variates, principal components, principal coordinates, correspondence analysis and discriminant functions. It is shown that there is a 'Moorish substrate' in the eastern and north-eastern parts of Spain and in southern Portugal. Serological effects, such as could derive from the assimilation of a large Jewish population, cannot be identified in the data available. The theory that most Hispano-Moslems and Spanish Jews were of indigenous origin is not gainsaid by the serological data available.

  1. ToF-SIMS imaging of PE/PP polymer using multivariate analysis

    Science.gov (United States)

    Miyasaka, Toyomitsu; Ikemoto, Takashi; Kohno, Teiichiro

    2008-12-01

    The distribution of polyethylene (PE) and polypropylene (PP) in PE/PP blended-polymer film was determined by applying principal components analysis (PCA) and multivariate curve resolution (MCR) to time-of-flight secondary ion mass spectroscopy (ToF-SIMS) imaging, together with preprocessing by pixel binning, normalization, and autoscaling to increase image contrast by reducing topographic and charge-distribution effects. The PE/PP distribution was confirmed by MVA conducted on the image data over static limit. The MCR score with normalized-autoscaling was found to give the PE/PP distribution distinctly.

  2. APPLICATION OF MULTIVARIATE ANALYSIS OF TRANSMISSION SPECTRA TO IDENTIFY WINES WITH PROTECTED GEOGRAPHICAL INDICATION (IGP

    Directory of Open Access Journals (Sweden)

    M. A. Khodasevich

    2016-01-01

    Full Text Available The simulation is carried out of physical and chemical characteristics of the unblended varietal young Moldovan wine harvested in 2014 by the projection to latent structures of the transmission spectra in the range of 220–2500 nm. The achieved accuracy of the regression determining the parameters is appropriate for practical application purposes (from 5 % for alcohol strength to 30 % for tartaric acid content in red wines. The possibility is shown of solving the problem of verification of the protected geographical indication of wines (IGP – Indication Géographique Protégée by the multivariate analysis of broadband transmission spectra. 

  3. Multivariate data analysis as a tool in advanced quality monitoring in the food production chain

    DEFF Research Database (Denmark)

    Bro, R.; van den Berg, F.; Thybo, A.

    2002-01-01

    This paper summarizes some recent advances in mathematical modeling of relevance in advanced quality monitoring in the food production chain. Using chemometrics-multivariate data analysis - it is illustrated how to tackle problems in food science more efficiently and, moreover, solve problems...... that could not otherwise be handled before. The different mathematical models are all exemplified by food related subjects to underline the generic use of the models within the food chain. Applications will be given from meat, storage, vegetable characterization, fish quality monitoring and industrial food...

  4. Multivariate statistical analysis of surface water chemistry: A case study of Gharasoo River, Iran

    Directory of Open Access Journals (Sweden)

    MH Sayadi

    2014-09-01

    Full Text Available Regional water quality is a hot spot in the environmental sciences for inconsistency of pollutants. In this paper, the surface water quality of the Gharasoo River in western Iran is assessed incorporating multivariate statistical techniques. Parameters like EC, TDS, pH, HCO3-, Cl-, SO4 2-, Ca2+, Mg2+ and Na+ were analyzed. Principal component and factor analysis is showed the parameters generated 3 significant factors, which explained 73.06% of the variance in data sets. Factor 1 may be derived from agricultural activities and subsequent release of EC, TDS, SO4 2- and Na+ to the water. Factor 2 could be influenced by domestic pollution and explained the deliverance of HCO3-, Cl- and Mg2+ into the water. Factor 3 contains hydro-geochemical variable Ca2+ and pH, originating from mineralization of the geological components of bed sediments and soils of watershed area. Likewise, the clustering analysis generated 3 groups of the stations as the groups had similar characteristic features. Pearson correlation analysis showed significant correlations between HCO3- and Mg2+ (0.775, Ca2+ (0.552 as well as TDS and Na+ (0.726. With reference to multivariate statistical analyses it can be concluded that the agricultural, domestic and hydro-geochemical sources are releasing the pollutants into the Gharasoo River water.

  5. Feature extraction techniques using multivariate analysis for identification of lung cancer volatile organic compounds

    Science.gov (United States)

    Thriumani, Reena; Zakaria, Ammar; Hashim, Yumi Zuhanis Has-Yun; Helmy, Khaled Mohamed; Omar, Mohammad Iqbal; Jeffree, Amanina; Adom, Abdul Hamid; Shakaff, Ali Yeon Md; Kamarudin, Latifah Munirah

    2017-03-01

    In this experiment, three different cell cultures (A549, WI38VA13 and MCF7) and blank medium (without cells) as a control were used. The electronic nose (E-Nose) was used to sniff the headspace of cultured cells and the data were recorded. After data pre-processing, two different features were extracted by taking into consideration of both steady state and the transient information. The extracted data are then being processed by multivariate analysis, Linear Discriminant Analysis (LDA) to provide visualization of the clustering vector information in multi-sensor space. The Probabilistic Neural Network (PNN) classifier was used to test the performance of the E-Nose on determining the volatile organic compounds (VOCs) of lung cancer cell line. The LDA data projection was able to differentiate between the lung cancer cell samples and other samples (breast cancer, normal cell and blank medium) effectively. The features extracted from the steady state response reached 100% of classification rate while the transient response with the aid of LDA dimension reduction methods produced 100% classification performance using PNN classifier with a spread value of 0.1. The results also show that E-Nose application is a promising technique to be applied to real patients in further work and the aid of Multivariate Analysis; it is able to be the alternative to the current lung cancer diagnostic methods.

  6. Application of multivariate analysis toward biotech processes: case study of a cell-culture unit operation.

    Science.gov (United States)

    Kirdar, Alime Ozlem; Conner, Jeremy S; Baclaski, Jeffrey; Rathore, Anurag S

    2007-01-01

    This paper examines the feasibility of using multivariate data analysis (MVDA) for supporting some of the key activities that are required for successful manufacturing of biopharmaceutical products. These activities include scale-up, process comparability, process characterization, and fault diagnosis. Multivariate data analysis and modeling were performed using representative data from small-scale (2 L) and large-scale (2000 L) batches of a cell-culture process. Several input parameters (pCO2, pO2, glucose, pH, lactate, ammonium ions) and output parameters (purity, viable cell density, viability, osmolality) were evaluated in this analysis. Score plots, loadings plots, and VIP plots were utilized for assessing scale-up and comparability of the cell-culture process. Batch control charts were found to be useful for fault diagnosis during routine manufacturing. Finally, observations made from reviewing VIP plots were found to be in agreement with conclusions from process characterization studies demonstrating the effectiveness of MVDA as a tool for extracting process knowledge.

  7. Analysis of non-stationary turbulent flows using Multivariate EMD and Matching Pursuits

    Science.gov (United States)

    Mohan, Arvind; Agostini, Lionel; Gaitonde, Datta; Visbal, Miguel

    2016-11-01

    Time-series analysis of highly transient non-stationary turbulent flow is challenging. Traditional Fourier based techniques are generally difficult to apply because of the highly aperiodic nature of the data. Another significant obstacle is assimilating multivariate data, such as multiple variables at a location or from different sources in a flow-field. Such an analysis has the potential to identify sensitive events common among these sources. In this work, we explore two techniques to address these challenges - Multivariate Empirical Mode Decomposition and Matching Pursuits, on deep dynamic stall of a plunging airfoil in a mixed laminar-transitional-turbulent regime. Although primarily used for neuroscience applications, we use them in fluid mechanics and highlight their significant potential to overcome limitations of more traditional techniques. Application of these methods highlight different stages in the development of stall. A first stage shows development of 2-D boundary layer oscillations at frequencies similar to those associated with trailing edge vortices. Subsequently, new instabilities arise due to imminent separation. The separation bubble itself is characterized by relatively higher frequency content, and further analysis indicates its 3-D collapse.

  8. Water quality analysis of the Rapur area, Andhra Pradesh, South India using multivariate techniques

    Science.gov (United States)

    Nagaraju, A.; Sreedhar, Y.; Thejaswi, A.; Sayadi, Mohammad Hossein

    2016-11-01

    The groundwater samples from Rapur area were collected from different sites to evaluate the major ion chemistry. The large number of data can lead to difficulties in the integration, interpretation, and representation of the results. Two multivariate statistical methods, hierarchical cluster analysis (HCA) and factor analysis (FA), were applied to evaluate their usefulness to classify and identify geochemical processes controlling groundwater geochemistry. Four statistically significant clusters were obtained from 30 sampling stations. This has resulted two important clusters viz., cluster 1 (pH, Si, CO3, Mg, SO4, Ca, K, HCO3, alkalinity, Na, Na + K, Cl, and hardness) and cluster 2 (EC and TDS) which are released to the study area from different sources. The application of different multivariate statistical techniques, such as principal component analysis (PCA), assists in the interpretation of complex data matrices for a better understanding of water quality of a study area. From PCA, it is clear that the first factor (factor 1), accounted for 36.2% of the total variance, was high positive loading in EC, Mg, Cl, TDS, and hardness. Based on the PCA scores, four significant cluster groups of sampling locations were detected on the basis of similarity of their water quality.

  9. Metabolomic Fingerprinting of Romaneschi Globe Artichokes by NMR Spectroscopy and Multivariate Data Analysis.

    Science.gov (United States)

    de Falco, Bruna; Incerti, Guido; Pepe, Rosa; Amato, Mariana; Lanzotti, Virginia

    2016-09-01

    Globe artichoke (Cynara cardunculus L. var. scolymus L. Fiori) and cardoon (Cynara cardunculus L. var. altilis DC) are sources of nutraceuticals and bioactive compounds. To apply a NMR metabolomic fingerprinting approach to Cynara cardunculus heads to obtain simultaneous identification and quantitation of the major classes of organic compounds. The edible part of 14 Globe artichoke populations, belonging to the Romaneschi varietal group, were extracted to obtain apolar and polar organic extracts. The analysis was also extended to one species of cultivated cardoon for comparison. The (1) H-NMR of the extracts allowed simultaneous identification of the bioactive metabolites whose quantitation have been obtained by spectral integration followed by principal component analysis (PCA). Apolar organic extracts were mainly based on highly unsaturated long chain lipids. Polar organic extracts contained organic acids, amino acids, sugars (mainly inulin), caffeoyl derivatives (mainly cynarin), flavonoids, and terpenes. The level of nutraceuticals was found to be highest in the Italian landraces Bianco di Pertosa zia E and Natalina while cardoon showed the lowest content of all metabolites thus confirming the genetic distance between artichokes and cardoon. Metabolomic approach coupling NMR spectroscopy with multivariate data analysis allowed for a detailed metabolite profile of artichoke and cardoon varieties to be obtained. Relevant differences in the relative content of the metabolites were observed for the species analysed. This work is the first application of (1) H-NMR with multivariate statistics to provide a metabolomic fingerprinting of Cynara scolymus. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  10. Estimating the impact of environmental conditions on hatching results using multivariable analysis

    Directory of Open Access Journals (Sweden)

    IA Nääs

    2008-12-01

    Full Text Available Hatching results are directly related to environmental and biological surroundings. This research study aimed at evaluating the influence of incubation environmental conditions on hatchability and one-day-old chickling quality of five production flocks using multivariable analysis tool. The experiment was carried out in a commercial hatchery located in the state of São Paulo, Brazil. Environmental variables such as dry bulb temperature, relative humidity, carbon dioxide concentration, and number of colony forming units of fungi were recorded inside a broiler multi-stage setter, a hatcher after eggs transference, and a chick-processing room. The homogeneity of parameter distribution among quadrants inside the setter, the hatcher, and the chick room was tested using the non-parametric test of Kruskal-Wallis, and the fit analysis was applied. The multivariate analysis was applied using the Main Component Technique in order to identify possible correlations between environmental and production parameters. Three different groups were identified: the first group is represented by temperature, which was positively correlated both with good hatchability and good chick quality; the second group indicates that poor chick quality was positively correlated with air velocity and relative humidity increase. The third group, represented by carbon dioxide concentration and fungi colonies forming units, presented strong positive association with embryo mortality increase.

  11. Multivariate stochastic analysis for Monthly hydrological time series at Cuyahoga River Basin

    Science.gov (United States)

    zhang, L.

    2011-12-01

    Copula has become a very powerful statistic and stochastic methodology in case of the multivariate analysis in Environmental and Water resources Engineering. In recent years, the popular one-parameter Archimedean copulas, e.g. Gumbel-Houggard copula, Cook-Johnson copula, Frank copula, the meta-elliptical copula, e.g. Gaussian Copula, Student-T copula, etc. have been applied in multivariate hydrological analyses, e.g. multivariate rainfall (rainfall intensity, duration and depth), flood (peak discharge, duration and volume), and drought analyses (drought length, mean and minimum SPI values, and drought mean areal extent). Copula has also been applied in the flood frequency analysis at the confluences of river systems by taking into account the dependence among upstream gauge stations rather than by using the hydrological routing technique. In most of the studies above, the annual time series have been considered as stationary signal which the time series have been assumed as independent identically distributed (i.i.d.) random variables. But in reality, hydrological time series, especially the daily and monthly hydrological time series, cannot be considered as i.i.d. random variables due to the periodicity existed in the data structure. Also, the stationary assumption is also under question due to the Climate Change and Land Use and Land Cover (LULC) change in the fast years. To this end, it is necessary to revaluate the classic approach for the study of hydrological time series by relaxing the stationary assumption by the use of nonstationary approach. Also as to the study of the dependence structure for the hydrological time series, the assumption of same type of univariate distribution also needs to be relaxed by adopting the copula theory. In this paper, the univariate monthly hydrological time series will be studied through the nonstationary time series analysis approach. The dependence structure of the multivariate monthly hydrological time series will be

  12. Multivariate analysis for the optimization of polysaccharide-based nanoparticles prepared by self-assembly.

    Science.gov (United States)

    Pistone, Sara; Qoragllu, Dafina; Smistad, Gro; Hiorth, Marianne

    2016-10-01

    Polysaccharide-based nanoparticles are promising carriers for drug delivery applications. The particle size influences the biodistribution of the nanoparticles; hence size distributions and polydispersity index (PDI) are critical characteristics. However, the preparation of stable particles with a low PDI is a challenging task and is usually based on empirical trials. In this study, we report the use of multivariate evaluation to optimize the formulation factors for the preparation of alginate-zinc nanoparticles by ionotropic gelation. The PDI was selected as the response variable. Particle size, size distributions, zeta potential and pH of the samples were also recorded. Two full factorial (mixed-level) designs were analyzed by partial least squares regression (PLS). In the first design, the influence of the polysaccharide and the crosslinker concentrations were studied. The results revealed that size distributions with a low PDI were obtained by using a low polysaccharide concentrations (0.03-0.05%) and a zinc concentration of 0.03% (w/w). However, a high polysaccharide concentration can be advantageous for drug delivery systems. Therefore, in the second design, a high alginate concentration was used (0.09%) and a reduction in the PDI was obtained by simultaneously increasing the ionic strength of the solvent and the zinc concentration. The multivariate analysis also revealed the interaction between the factors in terms of their effects on the PDI; hence, compared to traditional univariate analyses, the multivariate analysis allowed us to obtain a more complete understanding of the effects of the factors scrutinized. In addition, the results are considered useful in order to avoid extensive empirical tests for future formulation studies. Copyright © 2016 Elsevier B.V. All rights reserved.

  13. [Determination of rutin, quercetin and kaempferol in Althaea rosea (L) Gavan for Uyghur medicine by high performance liquid chromatography].

    Science.gov (United States)

    Muhetaer, Tu'erhong; Resalat, Yimin; Chu, Ganghui; Yin, Xuebo; Munira, Abudukeremu

    2015-12-01

    Uyghur medicine is one important part of the national medicine system. Uyghur medicine modernization, namely the study of effective components with modern technologies, is the only way for the scientification, standardization, and industrialization of Uyghur medicine. Here we developed a selective extraction method for rutin, quercetin and kaempferol in Althaea rosea (L) Gavan. The three active species were determined by high performance liquid chromatography (HPLC) with HC-C18 column (250 mm x 4.6 mm, 5 μm) and the mobile phase of CH3OH-0.4% H3PO4 (50 :50, v/v). Rutin, quercetin and kaempferol were baseline separated with each other and the interference species with flow rate of 1.0 mL/min and column temperature of 30 degrees C. Under the optimal conditions, linear correlation were obtained in the mass concentration range of 12.5-150 μg/mL (r = 0.999 8) for rutin, 12.5-125 μg/mL (r = 0.999 9) for quercetin, and 12.5-125 μg/mL (r = 0.998 8) for kaempferol. The recoveries (n = 5) of rutin, quercetin and kaempferol were 100.25% ( RSD = 1.1%), 97.60% ( RSD = 0.47%) and 97.75% (RSD = 0.71%), respectively. The method can be used to determine the contents of rutin, quercetin and kaempferol in Althaea rosea (L) Gavan and provide the guidance for the analysis of the flavonoids in other Uyghur medicines.

  14. MULTIVARIATE STEPWISE LOGISTIC REGRESSION ANALYSIS ON RISK FACTORS OF VENTILATOR-ASSOCIATED PNEUMONIA IN COMPREHENSIVE ICU

    Institute of Scientific and Technical Information of China (English)

    管军; 杨兴易; 赵良; 林兆奋; 郭昌星; 李文放

    2003-01-01

    Objective To investigate the incidence, crude mortality and independent risk factors of ventilator-associated pneumonia (VAP) in comprehensive ICU in China.Methods The clinical and microbiological data were retrospectively collected and analysed of all the 97 patients receiving mechanical ventilation (>48hr) in our comprehensive ICU during 1999. 1 - 2000. 12. Firstly several statistically significant risk factors were screened out with univariate analysis, then independent risk factors were determined with multivariate stepwise logistic regression analysis.Results The incidence of VAP was 54. 64% (15. 60 cases per 1000 ventilation days), the crude mortality 47.42% . Interval between the establishment of artificial airway and diagnosis of VAP was 6.9 ± 4.3 d. Univariate analysis suggested that indwelling naso-gastric tube, corticosteroid, acid inhibitor, third-generation cephalosporin/ imipenem, non - infection lung disease, and extrapulmonary infection were the statistically significant risk factors of

  15. The discrimination of honey origin using melissopalynology and Raman spectroscopy techniques coupled with multivariate analysis.

    Science.gov (United States)

    Corvucci, Francesca; Nobili, Lara; Melucci, Dora; Grillenzoni, Francesca-Vittoria

    2015-02-15

    Honey traceability to food quality is required by consumers and food control institutions. Melissopalynologists traditionally use percentages of nectariferous pollens to discriminate the botanical origin and the entire pollen spectrum (presence/absence, type and quantities and association of some pollen types) to determinate the geographical origin of honeys. To improve melissopalynological routine analysis, principal components analysis (PCA) was used. A remarkable and innovative result was that the most significant pollens for the traditional discrimination of the botanical and geographical origin of honeys were the same as those individuated with the chemometric model. The reliability of assignments of samples to honey classes was estimated through explained variance (85%). This confirms that the chemometric model properly describes the melissopalynological data. With the aim to improve honey discrimination, FT-microRaman spectrography and multivariate analysis were also applied. Well performing PCA models and good agreement with known classes were achieved. Encouraging results were obtained for botanical discrimination.

  16. A multivariate partial least squares approach to joint association analysis for multiple correlated traits

    Directory of Open Access Journals (Sweden)

    Yang Xu

    2016-02-01

    Full Text Available Many complex traits are highly correlated rather than independent. By taking the correlation structure of multiple traits into account, joint association analyses can achieve both higher statistical power and more accurate estimation. To develop a statistical approach to joint association analysis that includes allele detection and genetic effect estimation, we combined multivariate partial least squares regression with variable selection strategies and selected the optimal model using the Bayesian Information Criterion (BIC. We then performed extensive simulations under varying heritabilities and sample sizes to compare the performance achieved using our method with those obtained by single-trait multilocus methods. Joint association analysis has measurable advantages over single-trait methods, as it exhibits superior gene detection power, especially for pleiotropic genes. Sample size, heritability, polymorphic information content (PIC, and magnitude of gene effects influence the statistical power, accuracy and precision of effect estimation by the joint association analysis.

  17. A multivariate partial least squares approach to joint association analysis for multiple correlated traits

    Institute of Scientific and Technical Information of China (English)

    Yang Xu; Wenming Hu; Zefeng Yang; Chenwu Xu

    2016-01-01

    Many complex traits are highly correlated rather than independent. By taking the correlation structure of multiple traits into account, joint association analyses can achieve both higher statistical power and more accurate estimation. To develop a statistical approach to joint association analysis that includes allele detection and genetic effect estimation, we combined multivariate partial least squares regression with variable selection strategies and selected the optimal model using the Bayesian Information Criterion(BIC). We then performed extensive simulations under varying heritabilities and sample sizes to compare the performance achieved using our method with those obtained by single-trait multilocus methods. Joint association analysis has measurable advantages over single-trait methods, as it exhibits superior gene detection power, especially for pleiotropic genes. Sample size, heritability,polymorphic information content(PIC), and magnitude of gene effects influence the statistical power, accuracy and precision of effect estimation by the joint association analysis.

  18. A multivariate partial least squares approach to joint association analysis for multiple correlated traits

    Institute of Scientific and Technical Information of China (English)

    Yang Xu; Wenming Hu; Zefeng Yang; Chenwu Xu

    2016-01-01

    Many complex traits are highly correlated rather than independent. By taking the correlation structure of multiple traits into account, joint association analyses can achieve both higher statistical power and more accurate estimation. To develop a statistical approach to joint association analysis that includes allele detection and genetic effect estimation, we combined multivariate partial least squares regression with variable selection strategies and selected the optimal model using the Bayesian Information Criterion (BIC). We then performed extensive simulations under varying heritabilities and sample sizes to compare the performance achieved using our method with those obtained by single-trait multilocus methods. Joint association analysis has measurable advantages over single-trait methods, as it exhibits superior gene detection power, especially for pleiotropic genes. Sample size, heritability, polymorphic information content (PIC), and magnitude of gene effects influence the statistical power, accuracy and precision of effect estimation by the joint association analysis.

  19. Multivariate Multi-Scale Permutation Entropy for Complexity Analysis of Alzheimer’s Disease EEG

    Directory of Open Access Journals (Sweden)

    Isabella Palamara

    2012-07-01

    Full Text Available An original multivariate multi-scale methodology for assessing the complexity of physiological signals is proposed. The technique is able to incorporate the simultaneous analysis of multi-channel data as a unique block within a multi-scale framework. The basic complexity measure is done by using Permutation Entropy, a methodology for time series processing based on ordinal analysis. Permutation Entropy is conceptually simple, structurally robust to noise and artifacts, computationally very fast, which is relevant for designing portable diagnostics. Since time series derived from biological systems show structures on multiple spatial-temporal scales, the proposed technique can be useful for other types of biomedical signal analysis. In this work, the possibility of distinguish among the brain states related to Alzheimer’s disease patients and Mild Cognitive Impaired subjects from normal healthy elderly is checked on a real, although quite limited, experimental database.

  20. Multivariate analysis of the scattering profiles of healthy and pathological human breast tissues

    Energy Technology Data Exchange (ETDEWEB)

    Conceicao, A.L.C.; Antoniassi, M. [Departamento de Fisica e Matematica, FFCLRP, Universidade de Sao Paulo, Ribeirao Preto 14040-901, Sao Paulo (Brazil); Cunha, D.M. [Instituto de Fisica, Universidade Federal de Uberlandia, 38400-902, Uberlandia, Minas Gerais (Brazil); Ribeiro-Silva, A. [Departamento de Patologia, HCFMRP, Universidade de Sao Paulo, Ribeirao Preto 14040-901, Sao Paulo (Brazil); Poletti, M.E., E-mail: poletti@ffclrp.usp.br [Departamento de Fisica e Matematica, FFCLRP, Universidade de Sao Paulo, Ribeirao Preto 14040-901, Sao Paulo (Brazil)

    2011-10-01

    Scattering profiles of 106 healthy and pathological human breast samples were obtained using the angular dispersive X-ray scattering technique (AD-XRD) and synchrotron radiation covering the momentum transfer interval of 0.7 nm{sup -1}{<=}q(=4{pi} sin({theta}/2)/{lambda}){<=}70.5 nm{sup -1}. Multivariate analysis in the form of discriminant analysis was applied over the whole scattering profile curve of each sample in order to build a model for breast tissue classification. The classification results were validated and compared with histological sample classification obtained by microscopy analysis. Finally, the model allows classifying correctly 91.5% of the samples and presented values of 98.5%, 89.7% and 0.90 for sensitivity, specificity and Cohen's {kappa}, respectively, in correctly differentiating between healthy and pathological tissues.

  1. A multivariate statistical study with a factor analysis of recent planktonic foraminiferal distribution in the Coromandel Coast of India

    Digital Repository Service at National Institute of Oceanography (India)

    Jayalakshmy, K.V.; Rao, K.K.

    A study of planktonic foraminiferal assemblages from 19 stations in the neritic and oceanic regions off the Coromandel Coast, Bay of Bengal has been made using a multivariate statistical method termed as factor analysis. On the basis of abundance...

  2. Multivariate analysis of combining ability for soybean resistance to Cercospora sojina Hara

    Directory of Open Access Journals (Sweden)

    Geraldo de Amaral Gravina

    2004-01-01

    Full Text Available Seven soybean cultivars (Bossier, Cristalina, Davis, Kent, Lincoln, Paraná and Uberaba, with different levels of resistance to Cercospora sojina, race 04, were crossed according to a diallel design, with no reciprocals, to determine the general and the specific combining abilities for the resistance. The evaluations of the reaction to the disease were performed 20 days after the inoculation of the fungus on the most infected leaflet of the plant, in the parents and in the F1 hybrids. To quantify the resistance, the following characteristics were evaluated: infection degree (ID; number of lesions per leaflet (NLL; lesion mean diameter (LMD; lesioned leaf area (LLA; percentage of lesioned leaf area (PLLA; number of lesions per square centimeter (NLC and disease index (DI. The relative importance of each characteristic was evaluated by the canonical variables analysis and the LLA and NLL characteristics were eliminated from the multivariate function. With the remaining five characteristics, a multivariate index was created using the first canonical vector, which was submitted to the diallel analysis, according to Griffings fixed model, method 2. The most important characters to discriminate resistant from susceptible soybean plants to C. sojina were: ID, LMD, NLC, DI and PLLA. Cristalina, Davis and Uberaba cultivars are the best ones among those tested that can be recommended as parents in soybean breeding programs seeking resistance to Cercospora sojina. The additive, dominant and epistatic genetic effects were important for the expression of the resistance, although the additive genetic effect was the most important component.

  3. Use of multivariate analysis in mineral accumulation of rocket (Eruca sativa accessions

    Directory of Open Access Journals (Sweden)

    Bozokalfa Kadri M.

    2011-01-01

    Full Text Available The leafy vegetables contain high amount of mineral elements and health promoting compound. To solve nutritional problems in diet and reduced malnutrition among human population selection of specific cultivar among species would be help increasing elemental delivery in the human diet. While rocket plant observes several nutritional compounds no significant efforts have been made for genetic diversity for mineral composition of rocket plant accessions using multivariate analyses technique. The objective of this work was to evaluate variability for mineral accumulation of rocket accessions revealed by multivariate analysis to use further breeding program for achieve improving cultivar in targeting high nutrient concentration. A total twelve mineral element and twenty-three E. sativa accessions were investigated and considerable variation were observed in the most of concentration the principal component analysis explained that 77.67% of total variation accounted for four PC axis. Rocket accessions were classifies into three groups and present outcomes of experiments revealed that the first three principal components were highly valid to classify the examined accessions and separating mineral accumulations. Significant differences exhibited in mineral concentration among examined rocket accessions and the result could allow selecting those genotypes with higher elements.

  4. Sustainability Multivariate Analysis of the Energy Consumption of Ecuador Using MuSIASEM and BIPLOT Approach

    Directory of Open Access Journals (Sweden)

    Nathalia Tejedor-Flores

    2017-06-01

    Full Text Available Rapid economic growth, expanding populations and increasing prosperity are driving up demand for energy, water and food, especially in developing countries. To understand the energy consumption of a country, we used the Multi-Scale Integrated Analysis of Societal and Ecosystem Metabolism (MuSIASEM approach. The MuSIASEM is an innovative approach to accounting that integrates quantitative information generated by distinct types of conventional models based on different dimensions and scales of analysis. The main objective of this work is to enrich the MuSIASEM approach with information from multivariate methods in order to improve the efficiency of existing models of sustainability. The Biplot method permits the joint plotting, in a reduced dimension of the rows (individuals and columns (variables of a multivariate data matrix. We found, in the case study of Ecuador, that the highest values of the Exosomatic Metabolic Rate (EMR per economic sector and Economic Labor Productivity (ELP are located in the Productive Sector (PS. We conclude that the combination of the MuSIASEM variables with the HJ-Biplot allows us to easily know the detailed behavior of the labor productivity and energy consumption of a country.

  5. Implementation of multivariate linear mixed-effects models in the analysis of indoor climate performance experiments

    DEFF Research Database (Denmark)

    Jensen, Kasper Lynge; Spliid, Henrik; Toftum, Jørn

    2011-01-01

    The aim of the current study was to apply multivariate mixed-effects modeling to analyze experimental data on the relation between air quality and the performance of office work. The method estimates in one step the effect of the exposure on a multi-dimensional response variable, and yields impor....... The analysis seems superior to conventional univariate statistics and the information provided may be important for the design of performance experiments in general and for the conclusions that can be based on such studies.......The aim of the current study was to apply multivariate mixed-effects modeling to analyze experimental data on the relation between air quality and the performance of office work. The method estimates in one step the effect of the exposure on a multi-dimensional response variable, and yields...... important information on the correlation between the different dimensions of the response variable, which in this study was composed of both subjective perceptions and a two-dimensional performance task outcome. Such correlation is typically not included in the output from univariate analysis methods. Data...

  6. Multivariate Bayesian analysis of Gaussian, right censored Gaussian, ordered categorical and binary traits using Gibbs sampling

    Directory of Open Access Journals (Sweden)

    Madsen Per

    2003-03-01

    Full Text Available Abstract A fully Bayesian analysis using Gibbs sampling and data augmentation in a multivariate model of Gaussian, right censored, and grouped Gaussian traits is described. The grouped Gaussian traits are either ordered categorical traits (with more than two categories or binary traits, where the grouping is determined via thresholds on the underlying Gaussian scale, the liability scale. Allowances are made for unequal models, unknown covariance matrices and missing data. Having outlined the theory, strategies for implementation are reviewed. These include joint sampling of location parameters; efficient sampling from the fully conditional posterior distribution of augmented data, a multivariate truncated normal distribution; and sampling from the conditional inverse Wishart distribution, the fully conditional posterior distribution of the residual covariance matrix. Finally, a simulated dataset was analysed to illustrate the methodology. This paper concentrates on a model where residuals associated with liabilities of the binary traits are assumed to be independent. A Bayesian analysis using Gibbs sampling is outlined for the model where this assumption is relaxed.

  7. What makes a pattern? Matching decoding methods to data in multivariate pattern analysis.

    Science.gov (United States)

    Kragel, Philip A; Carter, R McKell; Huettel, Scott A

    2012-01-01

    Research in neuroscience faces the challenge of integrating information across different spatial scales of brain function. A promising technique for harnessing information at a range of spatial scales is multivariate pattern analysis (MVPA) of functional magnetic resonance imaging (fMRI) data. While the prevalence of MVPA has increased dramatically in recent years, its typical implementations for classification of mental states utilize only a subset of the information encoded in local fMRI signals. We review published studies employing multivariate pattern classification since the technique's introduction, which reveal an extensive focus on the improved detection power that linear classifiers provide over traditional analysis techniques. We demonstrate using simulations and a searchlight approach, however, that non-linear classifiers are capable of extracting distinct information about interactions within a local region. We conclude that for spatially localized analyses, such as searchlight and region of interest, multiple classification approaches should be compared in order to match fMRI analyses to the properties of local circuits.

  8. Multivariate Bayesian analysis of Gaussian, right censored Gaussian, ordered categorical and binary traits using Gibbs sampling.

    Science.gov (United States)

    Korsgaard, Inge Riis; Lund, Mogens Sandø; Sorensen, Daniel; Gianola, Daniel; Madsen, Per; Jensen, Just

    2003-01-01

    A fully Bayesian analysis using Gibbs sampling and data augmentation in a multivariate model of Gaussian, right censored, and grouped Gaussian traits is described. The grouped Gaussian traits are either ordered categorical traits (with more than two categories) or binary traits, where the grouping is determined via thresholds on the underlying Gaussian scale, the liability scale. Allowances are made for unequal models, unknown covariance matrices and missing data. Having outlined the theory, strategies for implementation are reviewed. These include joint sampling of location parameters; efficient sampling from the fully conditional posterior distribution of augmented data, a multivariate truncated normal distribution; and sampling from the conditional inverse Wishart distribution, the fully conditional posterior distribution of the residual covariance matrix. Finally, a simulated dataset was analysed to illustrate the methodology. This paper concentrates on a model where residuals associated with liabilities of the binary traits are assumed to be independent. A Bayesian analysis using Gibbs sampling is outlined for the model where this assumption is relaxed.

  9. Beer fermentation: monitoring of process parameters by FT-NIR and multivariate data analysis.

    Science.gov (United States)

    Grassi, Silvia; Amigo, José Manuel; Lyndgaard, Christian Bøge; Foschino, Roberto; Casiraghi, Ernestina

    2014-07-15

    This work investigates the capability of Fourier-Transform near infrared (FT-NIR) spectroscopy to monitor and assess process parameters in beer fermentation at different operative conditions. For this purpose, the fermentation of wort with two different yeast strains and at different temperatures was monitored for nine days by FT-NIR. To correlate the collected spectra with °Brix, pH and biomass, different multivariate data methodologies were applied. Principal component analysis (PCA), partial least squares (PLS) and locally weighted regression (LWR) were used to assess the relationship between FT-NIR spectra and the abovementioned process parameters that define the beer fermentation. The accuracy and robustness of the obtained results clearly show the suitability of FT-NIR spectroscopy, combined with multivariate data analysis, to be used as a quality control tool in the beer fermentation process. FT-NIR spectroscopy, when combined with LWR, demonstrates to be a perfectly suitable quantitative method to be implemented in the production of beer.

  10. Kaempferol inhibits the growth and metastasis of cholangiocarcinoma in vitro and in vivo.

    Science.gov (United States)

    Qin, Youyou; Cui, Wu; Yang, Xuewei; Tong, Baifeng

    2016-03-01

    Kaempferol is a flavonoid that has been reported to exhibit antitumor activity in various malignant tumors. However, the role of kaempferol on cholangiocarcinoma (CCA) is largely unknown. In this article, we found that kaempferol inhibited proliferation, reduced colony formation ability, and induced apoptosis in HCCC9810 and QBC939 cells in vitro. Results from transwell assay and wound-healing assay demonstrated that kaempferol significantly suppressed the migration and invasion abilities of HCCC9810 and QBC939 cells in vitro. Kaempferol was found to decrease the expression of Bcl-2 and increase the expressions of Bax, Fas, cleaved-caspase 3, cleaved-caspase 8, cleaved-caspase 9, and cleaved-PARP. In addition, kaempferol also downregulated the levels of phosphorylated AKT, TIMP2, and MMP2. In vivo, it was found that the volume of subcutaneous xenograft (0.15 cm(3)) in the kaempferol-treated group was smaller than that (0.6 cm(3)) in the control group. Kaempferol also suppressed the number and volume of metastasis foci in the lung metastasis model, with no marked effects on body weight of mice. Immunohistochemistry assay showed that the number of Ki-67-positive cells was lower in the kaempferol-treated group than that in the control group. We further confirmed that the changes of apoptosis- and invasion-related proteins after kaempferol treatment in vivo were similar to the results in vitro. These data suggest that kaempferol may be a promising candidate agent for the treatment of CCA.

  11. Pleiotropy analysis of quantitative traits at gene level by multivariate functional linear models.

    Science.gov (United States)

    Wang, Yifan; Liu, Aiyi; Mills, James L; Boehnke, Michael; Wilson, Alexander F; Bailey-Wilson, Joan E; Xiong, Momiao; Wu, Colin O; Fan, Ruzong

    2015-05-01

    In genetics, pleiotropy describes the genetic effect of a single gene on multiple phenotypic traits. A common approach is to analyze the phenotypic traits separately using univariate analyses and combine the test results through multiple comparisons. This approach may lead to low power. Multivariate functional linear models are developed to connect genetic variant data to multiple quantitative traits adjusting for covariates for a unified analysis. Three types of approximate F-distribution tests based on Pillai-Bartlett trace, Hotelling-Lawley trace, and Wilks's Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants in one genetic region. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and optimal sequence kernel association test (SKAT-O). Extensive simulations were performed to evaluate the false positive rates and power performance of the proposed models and tests. We show that the approximate F-distribution tests control the type I error rates very well. Overall, simultaneous analysis of multiple traits can increase power performance compared to an individual test of each trait. The proposed methods were applied to analyze (1) four lipid traits in eight European cohorts, and (2) three biochemical traits in the Trinity Students Study. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and SKAT-O for the three biochemical traits. The approximate F-distribution tests of the proposed functional linear models are more sensitive than those of the traditional multivariate linear models that in turn are more sensitive than SKAT-O in the univariate case. The analysis of the four lipid traits and the three biochemical traits detects more association than SKAT-O in the univariate case.

  12. Kaempferol glycosides in the flowers of carnation and their contribution to the creamy white flower color.

    Science.gov (United States)

    Iwashina, Tsukasa; Yamaguchi, Masa-atsu; Nakayama, Masayoshi; Onozaki, Takashi; Yoshida, Hiroyuki; Kawanobu, Shuji; Onoe, Hiroshi; Okamura, Masachika

    2010-12-01

    Three flavonol glycosides were isolated from the flowers of carnation cultivars 'White Wink' and 'Honey Moon'. They were identified from their UV, MS, 1H and 13C NMR spectra as kaempferol 3-O-neohesperidoside, kaempferol 3-O-sophoroside and kaempferol 3-O-glucosyl-(1 --> 2)-[rhamnosyl-(1 --> 6)-glucoside]. Referring to previous reports, flavonols occurring in carnation flowers are characterized as kaempferol 3-O-glucosides with additional sugars binding at the 2 and/or 6-positions of the glucose. The kaempferol glycoside contents of a nearly pure white flower and some creamy white flower lines were compared. Although the major glycoside was different in each line, the total kaempferol contents of the creamy white lines were from 5.9 to 20.9 times higher than the pure white line. Thus, in carnations, kaempferol glycosides surely contribute to the creamy tone of white flowers.

  13. Kaempferol enhances the suppressive function of Treg cells by inhibiting FOXP3 phosphorylation.

    Science.gov (United States)

    Lin, Fang; Luo, Xuerui; Tsun, Andy; Li, Zhiyuan; Li, Dan; Li, Bin

    2015-10-01

    Kaempferol is a natural flavonoid found in many vegetables and fruits. Epidemiologic studies have described that Kaempferol intake could reduce risk of cancer, especially lung, gastric, pancreatic and ovarian cancers. Recent studies have shown that Kaempferol could also be beneficial to the body to defend against inflammation, and infection by bacteria and viruses; however, the molecular mechanism of its immunoregulatory function remains largely unknown. Through screening a small molecule library of traditional Chinese medicine (TCM), we identified that Kaempferol could enhance the suppressive function of regulatory T cells (Tregs). Kaempferol was found to increase FOXP3 expression level in Treg cells and prevent pathological symptoms of collagen-induced arthritis in a rat animal model. Kaempferol could also reduce PIM1-mediated FOXP3 phosphorylation at S422. Our study reveals a molecular mechanism that underlies the anti-inflammatory action of Kaempferol for the prevention and treatment of inflammatory diseases such as rheumatoid arthritis, systemic lupus erythematosus, and ankylosing spondylitis.

  14. Surgical workflow analysis with Gaussian mixture multivariate autoregressive (GMMAR) models: a simulation study.

    Science.gov (United States)

    Loukas, Constantinos; Georgiou, Evangelos

    2013-01-01

    There is currently great interest in analyzing the workflow of minimally invasive operations performed in a physical or simulation setting, with the aim of extracting important information that can be used for skills improvement, optimization of intraoperative processes, and comparison of different interventional strategies. The first step in achieving this goal is to segment the operation into its key interventional phases, which is currently approached by modeling a multivariate signal that describes the temporal usage of a predefined set of tools. Although this technique has shown promising results, it is challenged by the manual extraction of the tool usage sequence and the inability to simultaneously evaluate the surgeon's skills. In this paper we describe an alternative methodology for surgical phase segmentation and performance analysis based on Gaussian mixture multivariate autoregressive (GMMAR) models of the hand kinematics. Unlike previous work in this area, our technique employs signals from orientation sensors, attached to the endoscopic instruments of a virtual reality simulator, without considering which tools are employed at each time-step of the operation. First, based on pre-segmented hand motion signals, a training set of regression coefficients is created for each surgical phase using multivariate autoregressive (MAR) models. Then, a signal from a new operation is processed with GMMAR, wherein each phase is modeled by a Gaussian component of regression coefficients. These coefficients are compared to those of the training set. The operation is segmented according to the prior probabilities of the surgical phases estimated via GMMAR. The method also allows for the study of motor behavior and hand motion synchronization demonstrated in each phase, a quality that can be incorporated into modern laparoscopic simulators for skills assessment.

  15. Application of bioreactor design principles and multivariate analysis for development of cell culture scale down models.

    Science.gov (United States)

    Tescione, Lia; Lambropoulos, James; Paranandi, Madhava Ram; Makagiansar, Helena; Ryll, Thomas

    2015-01-01

    A bench scale cell culture model representative of manufacturing scale (2,000 L) was developed based on oxygen mass transfer principles, for a CHO-based process producing a recombinant human protein. Cell culture performance differences across scales are characterized most often by sub-optimal performance in manufacturing scale bioreactors. By contrast in this study, reduced growth rates were observed at bench scale during the initial model development. Bioreactor models based on power per unit volume (P/V), volumetric mass transfer coefficient (kL a), and oxygen transfer rate (OTR) were evaluated to address this scale performance difference. Lower viable cell densities observed for the P/V model were attributed to higher sparge rates and reduced oxygen mass transfer efficiency (kL a) of the small scale hole spargers. Increasing the sparger kL a by decreasing the pore size resulted in a further decrease in growth at bench scale. Due to sensitivity of the cell line to gas sparge rate and bubble size that was revealed by the P/V and kL a models, an OTR model based on oxygen enrichment and increased P/V was selected that generated endpoint sparge rates representative of 2,000 L scale. This final bench scale model generated similar growth rates as manufacturing. In order to take into account other routinely monitored process parameters besides growth, a multivariate statistical approach was applied to demonstrate validity of the small scale model. After the model was selected based on univariate and multivariate analysis, product quality was generated and verified to fall within the 95% confidence limit of the multivariate model.

  16. Effects of starch on nitrous acid-induced oxidation of kaempferol and inhibition of α-amylase-catalysed digestion of starch by kaempferol under conditions simulating the stomach and the intestine.

    Science.gov (United States)

    Takahama, Umeo; Hirota, Sachiko

    2013-11-01

    Kaempferol glycosides can be hydrolyzed to their aglycone kaempferol during cooking under acidic conditions and in the oral cavity and the intestine by glycosidases. Kaempferol was oxidised by nitrite under acidic conditions (pH 2.0) to produce nitric oxide (NO), and the nitrite-induced oxidation of kaempferol was enhanced and inhibited by 10 and 100mg of starch ml(-1), respectively. The opposite effects of starch were discussed by considering the binding of kaempferol to starch and starch-dependent inhibition of the accessibility of nitrous acid to kaempferol. Kaempferol inhibited α-amylase-catalysed starch digestion by forming starch/kaempferol complexes, and the inhibitory effects increased in the order of amylopectinkaempferol were discussed to be due to the difference in binding sites of kaempferol between amylose and amylopectin. From the present study, dual-function of kaempferol became apparent in the digestive tract.

  17. Probing beer aging chemistry by nuclear magnetic resonance and multivariate analysis

    Energy Technology Data Exchange (ETDEWEB)

    Rodrigues, J.A. [CICECO-Department of Chemistry, University of Aveiro, Campus de Santiago, 3810-193 Aveiro (Portugal); Barros, A.S. [QOPNA-Department of Chemistry, University of Aveiro, Campus de Santiago, 3810-193 Aveiro (Portugal); Carvalho, B.; Brandao, T. [UNICER, Bebidas de Portugal, Leca do Balio, 4466-955, S. Mamede de Infesta (Portugal); Gil, Ana M., E-mail: agil@ua.pt [CICECO-Department of Chemistry, University of Aveiro, Campus de Santiago, 3810-193 Aveiro (Portugal)

    2011-09-30

    Graphical abstract: The use of nuclear magnetic resonance (NMR) metabonomics for monitoring the chemical changes occurring in beer exposed to forced aging (at 45 deg. C for up to 18 days) is described. Both principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were applied to the NMR spectra of beer recorded as a function of aging and an aging trend was observed. Inspection of PLS-DA loadings and peak integration revealed the importance of well known markers (e.g. 5-HMF) as well as of other compounds: amino acids, higher alcohols, organic acids, dextrins and some still unassigned spin systems. 2D correlation analysis enabled relevant compound variations to be confirmed and inter-compound correlations to be assessed, thus offering improved insight into the chemical aspects of beer aging. Highlights: {center_dot} Use of NMR metabonomics for monitoring the chemical changes occurring in beer exposed to forced aging. {center_dot} Compositional variations evaluated by principal component analysis and partial least squares-discriminant analysis. {center_dot} Results reveal importance of known markers and other compounds: amino and organic acids, higher alcohols, dextrins. {center_dot} 2D correlation analysis reveals inter-compound relationships, offering insight into beer aging chemistry. - Abstract: This paper describes the use of nuclear magnetic resonance (NMR) spectroscopy, in tandem with multivariate analysis (MVA), for monitoring the chemical changes occurring in a lager beer exposed to forced aging (at 45 deg. C for up to 18 days). To evaluate the resulting compositional variations, both principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were applied to the NMR spectra of beer recorded as a function of aging and a clear aging trend was observed. Inspection of PLS-DA loadings and peak integration enabled the changing compounds to be identified, revealing the importance of well known

  18. Trace element distribution in human teeth by x-ray fluorescence spectrometry and multivariate statistical analysis

    CERN Document Server

    Oprea, Cristiana; Gustova, Marina V; Oprea, Ioan A; Buzguta, Violeta L

    2014-01-01

    X-ray fluorescence spectrometry (XRFS) was used as a multielement method of evaluation of individual whole human tooth or tooth tissues for their amounts of trace elements. Measurements were carried out on human enamel, dentine, and dental cementum, and some differences in tooth matrix composition were noted. In addition, the elemental concentrations determined in teeth from subjects of different ages, nutritional states, professions and gender, living under various environmental conditions and dietary habits, were included in a comparison by multivariate statistical analysis (MVSA) methods. By factor analysis it was established that inorganic components of human teeth varied consistently with their source in the tissue, with more in such tissue from females than in that from males, and more in tooth incisor than in tooth molar.

  19. Modeling and predicting spoilage of cooked, cured meat products by multivariate analysis.

    Science.gov (United States)

    Mataragas, Marios; Skandamis, Panagiotis; Nychas, George-John E; Drosinos, Eleftherios H

    2007-11-01

    A cooked, cured meat product is a perishable product spoiled mainly by lactic acid bacteria (LAB). LAB cause discoloration, slime formation, off-odors and off-flavors as the result of their metabolic activity producing various products. These microbial products in conjunction with the microbial population could be used to assess the degree of spoilage of this type of product. The spoilage evaluation was achieved by following a multivariate approach. Cluster analysis, principal component analysis and partial least square regression were employed to associate spoilage with microbiological and physicochemical parameters. The developed model was capable of giving accurate predictions of spoilage describing the spoilage associations. The study might contribute to the improvement of quality assurance systems of meat enterprises.

  20. Multivariate statistical analysis of stream-sediment geochemistry in the Grazer Paläozoikum, Austria

    Science.gov (United States)

    Weber, L.; Davis, J.C.

    1990-01-01

    The Austrian reconnaissance study of stream-sediment composition — more than 30000 clay-fraction samples collected over an area of 40000 km2 — is summarized in an atlas of regional maps that show the distributions of 35 elements. These maps, rich in information, reveal complicated patterns of element abundance that are difficult to compare on more than a small number of maps at one time. In such a study, multivariate procedures such as simultaneous R-Q mode components analysis may be helpful. They can compress a large number of variables into a much smaller number of independent linear combinations. These composite variables may be mapped and relationships sought between them and geological properties. As an example, R-Q mode components analysis is applied here to the Grazer Paläozoikum, a tectonic unit northeast of the city of Graz, which is composed of diverse lithologies and contains many mineral deposits.

  1. Investigation of the phase separation of PNIPAM using infrared spectroscopy together with multivariate data analysis

    DEFF Research Database (Denmark)

    Munk, Tommy; Baldursdottir, Stefania G.; Hietala, S.

    2013-01-01

    to gain an oversight of small but systematic spectral differences anywhere within the spectra, providing further insight into structural changes and associated transformation mechanisms. In this study, the novel analytical approach of infrared spectroscopy combined with principal component analysis...... a complex re-organization of the hydrogen bonds and change of the hydration layer. The changes agreed with existing results from other techniques, and new insights were gained into the effect of controlled tacticity on phase transformation behaviour. The study demonstrates that infrared spectroscopy......The use of vibrational spectroscopy to investigate complex structural changes in polymers yields chemically rich data, but interpretation can be challenging and subtle but meaningful spectral changes may be missed through visual inspection alone. Multivariate analysis is an efficient approach...

  2. A frailty model approach for regression analysis of multivariate current status data.

    Science.gov (United States)

    Chen, Man-Hua; Tong, Xingwei; Sun, Jianguo

    2009-11-30

    This paper discusses regression analysis of multivariate current status failure time data (The Statistical Analysis of Interval-censoring Failure Time Data. Springer: New York, 2006), which occur quite often in, for example, tumorigenicity experiments and epidemiologic investigations of the natural history of a disease. For the problem, several marginal approaches have been proposed that model each failure time of interest individually (Biometrics 2000; 56:940-943; Statist. Med. 2002; 21:3715-3726). In this paper, we present a full likelihood approach based on the proportional hazards frailty model. For estimation, an Expectation Maximization (EM) algorithm is developed and simulation studies suggest that the presented approach performs well for practical situations. The approach is applied to a set of bivariate current status data arising from a tumorigenicity experiment.

  3. Distinguishing Monosaccharide Stereo- and Structural Isomers with ToF-SIMS and Multivariate Statistical Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Berman, E F; Kulp, K S; Knize, M G; Wu, L; Nelson, E J; Nelson, D O; Wu, K J

    2006-05-04

    Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) is utilized to examine the mass spectra and fragmentation patterns of seven isomeric monosaccharides. Multivariate statistical analysis techniques, including principal component analysis (PCA), allow discrimination of the extremely similar mass spectra of stereoisomers. Furthermore, PCA identifies those fragment peaks which vary significantly between spectra. Heavy isotope studies confirm that these peaks are indeed sugar fragments, allow identification of the fragments, and provide clues to the fragmentation pathways. Excellent reproducibility is shown by multiple experiments performed over time and on separate samples. This study demonstrates the combined selectivity and discrimination power of ToF-SIMS and PCA, and suggests new applications of the technique including differentiation of subtle chemical changes in biological samples that may provide insights into cellular processes, disease progress, and disease diagnosis.

  4. The multivariate analysis of indications of rigid bronchoscopy in suspected foreign body aspiration.

    Science.gov (United States)

    Divarci, E; Toker, B; Dokumcu, Z; Musayev, A; Ozcan, C; Erdener, A

    2017-09-01

    Foreign body aspiration (FBA) could be a serious life-threatening condition in children. Patients usually underwent bronchoscopy with suspicious of FBA alone. In this study, we aimed to determine which patients need to go to bronchoscopy based on pre-operative findings. Retrospective analysis of patients underwent bronchoscopy between 1999 and 2015 was performed. Clinical symptoms, witnessed aspiration event (WAE), physical examination findings (PEFs) and radiological findings (RFs) were analyzed by multivariate analysis to evaluate the indications of bronchoscopy. 431 patients (266M, 165F) underwent bronchoscopy with a median age of 2 years (7 months-16 years). A foreign body was detected in 68% of the patients. Univariate analysis demonstrated that wheeze was the sole distinctive clinical symptom for detection of FBA (pMultivariate analysis was performed with considering the association between them. The rate of positive bronchoscopy was 91.3% in patients with positive WAE, PEFs and RFs together(84/92). In patients with a positive WAE alone who had not got PEFs and RFs, the rate of positive bronchoscopy was 34.2% (25/73). A foreign body was detected in 84% of the patients who had not got a WAE but positive PEFs and RFs together(21/25). Bronchial laceration was occurred in one patient during bronchoscopy. Pneumothorax was not seen in any of the other patients. The rate of mortality was 0.4% in the overall group (2 patients). The indications of bronchoscopy in suspected FBA are usually based on clinical suspicious. The definition of " suspicous" could be a WAE or positive PEFs and RFs. The association of these factors increase the rate of positive bronchoscopies. In the light of our study, the classical indication for suspected FBA is still valid as "suspicious requires bronchoscopy". Copyright © 2017 Elsevier B.V. All rights reserved.

  5. Factors associated with a more rapid recovery after anterior cruciate ligament reconstruction using multivariate analysis.

    Science.gov (United States)

    Scherer, Job E; Moen, Maarten H; Weir, Adam; Schmikli, Sandor L; Tamminga, Rob; van der Hoeven, Henk

    2016-01-01

    In the past, several studies investigated factors that are prognostic or associated with outcome after anterior cruciate ligament (ACL) reconstruction. A recent review showed that only limited evidence is available for most studied factors, and that insufficient analysis methods were used commonly. Therefore, the aim of this study was to add more weight to the existing evidence, about factors that are associated with a more rapid outcome after ACL reconstruction. The second aim was to use multivariate analysis to study the possible factors independently. A cohort study was conducted with a follow-up of six months. Before surgery, patient variables were scored. Surgical variables were scored during arthroscopic ACL reconstructions with a single-bundle technique and hamstring autograft. The Lysholm score and subscales of the Knee Injury Osteoarthritis Outcome Score (KOOS) were assessed six months post surgery. A multiple analysis of variance (ANOVA) model was used to identify prognostic factors for outcome. In total, 118 patients were included. Patients, aged ≤30years, with a subjective knee score ≥ six, with normal flexion range of motion (ROM) of the knee, with flexion and extension strength deficit of ≤20%, and those with no previous knee surgery in the same knee at baseline scored significantly higher on outcome after multivariate analysis. No significant effect of surgical factors could be found. Younger age, higher subjective knee score, normal knee flexion, normal knee flexion and extension strength, and no previous knee surgery in the patients' history at baseline are associated with a more rapid recovery after ACL reconstruction. Level III, prognostic study. Copyright © 2015 Elsevier B.V. All rights reserved.

  6. Multivariate Statistical Analysis of Water Quality data in Indian River Lagoon, Florida

    Science.gov (United States)

    Sayemuzzaman, M.; Ye, M.

    2015-12-01

    The Indian River Lagoon, is part of the longest barrier island complex in the United States, is a region of particular concern to the environmental scientist because of the rapid rate of human development throughout the region and the geographical position in between the colder temperate zone and warmer sub-tropical zone. Thus, the surface water quality analysis in this region always brings the newer information. In this present study, multivariate statistical procedures were applied to analyze the spatial and temporal water quality in the Indian River Lagoon over the period 1998-2013. Twelve parameters have been analyzed on twelve key water monitoring stations in and beside the lagoon on monthly datasets (total of 27,648 observations). The dataset was treated using cluster analysis (CA), principle component analysis (PCA) and non-parametric trend analysis. The CA was used to cluster twelve monitoring stations into four groups, with stations on the similar surrounding characteristics being in the same group. The PCA was then applied to the similar groups to find the important water quality parameters. The principal components (PCs), PC1 to PC5 was considered based on the explained cumulative variances 75% to 85% in each cluster groups. Nutrient species (phosphorus and nitrogen), salinity, specific conductivity and erosion factors (TSS, Turbidity) were major variables involved in the construction of the PCs. Statistical significant positive or negative trends and the abrupt trend shift were detected applying Mann-Kendall trend test and Sequential Mann-Kendall (SQMK), for each individual stations for the important water quality parameters. Land use land cover change pattern, local anthropogenic activities and extreme climate such as drought might be associated with these trends. This study presents the multivariate statistical assessment in order to get better information about the quality of surface water. Thus, effective pollution control/management of the surface

  7. Multivariate co-integration analysis of the Kaya factors in Ghana.

    Science.gov (United States)

    Asumadu-Sarkodie, Samuel; Owusu, Phebe Asantewaa

    2016-05-01

    The fundamental goal of the Government of Ghana's development agenda as enshrined in the Growth and Poverty Reduction Strategy to grow the economy to a middle income status of US$1000 per capita by the end of 2015 could be met by increasing the labour force, increasing energy supplies and expanding the energy infrastructure in order to achieve the sustainable development targets. In this study, a multivariate co-integration analysis of the Kaya factors namely carbon dioxide, total primary energy consumption, population and GDP was investigated in Ghana using vector error correction model with data spanning from 1980 to 2012. Our research results show an existence of long-run causality running from population, GDP and total primary energy consumption to carbon dioxide emissions. However, there is evidence of short-run causality running from population to carbon dioxide emissions. There was a bi-directional causality running from carbon dioxide emissions to energy consumption and vice versa. In other words, decreasing the primary energy consumption in Ghana will directly reduce carbon dioxide emissions. In addition, a bi-directional causality running from GDP to energy consumption and vice versa exists in the multivariate model. It is plausible that access to energy has a relationship with increasing economic growth and productivity in Ghana.

  8. Bone dimensional variations at implants placed in fresh extraction sockets: a multilevel multivariate analysis.

    Science.gov (United States)

    Tomasi, Cristiano; Sanz, Mariano; Cecchinato, Denis; Pjetursson, Bjarni; Ferrus, Jorge; Lang, Niklaus P; Lindhe, Jan

    2010-01-01

    To use multilevel, multivariate models to analyze factors that may affect bone alterations during healing after an implant immediately placed into an extraction socket. Data included in the current analysis were obtained from a clinical trial in which a series of measurements were performed to characterize the extraction site immediately after implant installation and at re-entry 4 months later. A regression multilevel, multivariate model was built to analyze factors affecting the following variables: (i) the distance between the implant surface and the outer bony crest (S-OC), (ii) the horizontal residual gap (S-IC), (iii) the vertical residual gap (R-D) and (iv) the vertical position of the bone crest opposite the implant (R-C). It was demonstrated that (i) the S-OC change was significantly affected by the thickness of the bone crest; (ii) the size of the residual gap was dependent of the size of the initial gap and the thickness of the bone crest; and (iii) the reduction of the buccal vertical gap was dependent on the age of the subject. Moreover, the position of the implant opposite the alveolar crest of the buccal ridge and its bucco-lingual implant position influenced the amount of buccal crest resorption. Clinicians must consider the thickness of the buccal bony wall in the extraction site and the vertical as well as the horizontal positioning of the implant in the socket, because these factors will influence hard tissue changes during healing.

  9. A Versatile Cell Death Screening Assay Using Dye-Stained Cells and Multivariate Image Analysis.

    Science.gov (United States)

    Collins, Tony J; Ylanko, Jarkko; Geng, Fei; Andrews, David W

    2015-11-01

    A novel dye-based method for measuring cell death in image-based screens is presented. Unlike conventional high- and medium-throughput cell death assays that measure only one form of cell death accurately, using multivariate analysis of micrographs of cells stained with the inexpensive mix, red dye nonyl acridine orange, and a nuclear stain, it was possible to quantify cell death induced by a variety of different agonists even without a positive control. Surprisingly, using a single known cytotoxic agent as a positive control for training a multivariate classifier allowed accurate quantification of cytotoxicity for mechanistically unrelated compounds enabling generation of dose-response curves. Comparison with low throughput biochemical methods suggested that cell death was accurately distinguished from cell stress induced by low concentrations of the bioactive compounds Tunicamycin and Brefeldin A. High-throughput image-based format analyses of more than 300 kinase inhibitors correctly identified 11 as cytotoxic with only 1 false positive. The simplicity and robustness of this dye-based assay makes it particularly suited to live cell screening for toxic compounds.

  10. Multivariable analysis of effects of various methods of magnesium application of LG 2244 maize hybrid

    Directory of Open Access Journals (Sweden)

    Jan Bocianowski

    2013-03-01

    Full Text Available The paper presents a multivariable approach to the estimation of variability for quantitative traits after using the seven methods of magnesium application of the “stay-green” type of maize (Zea mays L. hybrid. The 13 characteristics of LG 2244 hybrid were under consideration in three years (2006-2008: grain yield, moisture of grain, 1000 grain yield, dry matter of a single plant, dry matter yield, uptake of N, uptake of P, uptake of K, uptake of Mg, uptake of Ca, chlorophyll a, chlorophyll b and chlorophyll a+b content. The obtained results were computed with statistical multivariable methods of application. Canonical variable analysis (in each year independent has proved to be an effective tool for clear assessing of differences among the studied methods of magnesium application. The most diverse methods were: C and E (in 2006, A and F (in 2007, and B as G (in 2008. The most similar methods (in respect of 13 traits simultaneously were: B and C (in 2006, D and E (in 2007, and C and E (in 2008. Mahalanobis’ distances between methods of magnesium application in individual years of the study were not significantly correlated.

  11. Predicting malignant neck lymphadenopathy using color duplex sonography based on multivariate analysis.

    Science.gov (United States)

    Chammas, Maria C; Macedo, Túlio A A; Lo, Victor W; Gomes, Andrea C; Juliano, Adriana; Cerri, Giovanni G

    2016-11-12

    To select the best predictors of cervical lymph node malignancy based on gray-scale and power Doppler sonography using multivariate analysis. We evaluated sonographically a total of 97 lymph nodes in the neck that were subjected to fine-needle aspiration biopsy. The gray-scale and power Doppler sonography parameters that we analyzed using multivariate logistic regression included size, shape, echogenicity, echotexture, margins, hilum, presence of microcalcifications or necrosis, vascularization, and resistance index (RI). The three variables with a diagnostic accuracy exceeding 80% were an altered vascularization, heterogeneous echotexture, and abnormal hilum. Malignant nodes exhibited higher RI and larger sizes than benign nodes, and the best cutoff values to distinguish malignant from benign lymph nodes were an RI of 0.77 and a short axis ≥ 0.9 cm. Altered vascularization, a short axis ≥ 0.9 cm, and abnormal hilum were the best predictors of malignancy. The best sonographic predictors of lymph node malignancy are, in descending order, an altered vascularization, a short axis ≥ 0.9 cm, an abnormal hilum, and a heterogeneous echotexture. © 2016 Wiley Periodicals, Inc. J Clin Ultrasound 44:587-594, 2016. © 2016 Wiley Periodicals, Inc.

  12. Bioelectronic tongue and multivariate analysis: a next step in BOD measurements.

    Science.gov (United States)

    Raud, Merlin; Kikas, Timo

    2013-05-01

    Seven biosensors based on different semi-specific and universal microorganisms were constructed for biochemical oxygen demand (BOD) measurements in various synthetic industrial wastewaters. All biosensors were calibrated using OECD synthetic wastewater and the resulting calibration curves were used in the calculations of the sensor-BOD values for all biosensors. In addition, the output signals of all biosensors were analyzed as a bioelectronic tongue and comprehensive multivariate data analysis was applied to extract qualitative and quantitative information from the samples. In the case of individual biosensor measurements, most accurate result was gained when semi-specific biosensor was applied to analyze sample specific to that biosensor. Universal biosensors or biosensors semi-specific to other samples underestimated the BOD7 of the sample 10-25%. PLS regression method was used for the multivariate calibration of the biosensor array. The calculated sensor-BOD values differed from BOD7 less than 5.6% in all types of samples. By applying PCA and using three first principal components, giving 99.66% of variation, it was possible to differentiate samples by their compositions.

  13. Multivariate Analysis of Magnetic Resonance Imaging Signals of the Human Brain.

    Science.gov (United States)

    Miyawaki, Yoichi

    2016-01-01

    Magnetic resonance imaging (MRI) of the human brain plays an important role in the field of medical imaging as well as basic neuroscience. It measures proton spin relaxation, the time constant of which depends on tissue type, and allows us to visualize anatomical structures in the brain. It can also measure functional signals that depend on the local ratio of oxyhemoglobin to deoxyhemoglobin in the blood, which is believed to reflect the degree of neural activity in the corresponding area. MRI thus provides anatomical and functional information about the human brain with high spatial resolution. Conventionally, MRI signals are measured and analyzed for each individual voxel. However, these signals are essentially multivariate because they are measured from multiple voxels simultaneously, and the pattern of activity might carry more useful information than each individual voxel does. This paper reviews recent trends in multivariate analysis of MRI signals in the human brain, and discusses applications of this technique in the fields of medical imaging and neuroscience.

  14. Multivariate analysis of prognostic factors for salvage nasopharyngectomy via the maxillary swing approach.

    Science.gov (United States)

    Chan, Jimmy Yu Wai; To, Victor Shing Howe; Chow, Velda Ling Yu; Wong, Stanley Thian Sze; Wei, William Ignace

    2014-07-01

    The purpose of this study was to investigate the prognostic factors for salvage nasopharyngectomy. A retrospective review was conducted on maxillary swing nasopharyngectomy performed between 1998 and 2010. Univariate and multivariate analyses identified prognostic factors affecting actuarial local tumor control and overall survival. The median follow-up duration was 52 months. Among the 268 patients, 79.1% had clear resection margins. The 5-year actuarial local tumor control and overall survival was 74% and 62.1%, respectively. On multivariate analysis, tumor size, resection margin status, and gross tumor in the sphenoid sinus were independent prognostic factors for local tumor control. For overall survival, resection margin status, synchronous cervical nodal recurrence, and cavernous sinus invasion had a negative influence on overall survival after surgery. Extent of nasopharyngectomy should be tailored to the individual tumor to achieve clear resection margins. Cavernous sinus invasion is associated with poor survival outcome, and detailed counseling and meticulous surgical planning is crucial in such circumstances. Copyright © 2014 Wiley Periodicals, Inc.

  15. Multivariate analysis of risk factors for QT prolongation following subarachnoid hemorrhage

    Science.gov (United States)

    Fukui, Shinji; Katoh, Hiroshi; Tsuzuki, Nobusuke; Ishihara, Shoichiro; Otani, Naoki; Ooigawa, Hidetoshi; Toyooka, Terushige; Ohnuki, Akira; Miyazawa, Takahito; Nawashiro, Hiroshi; Shima, Katsuji

    2003-01-01

    Background Subarachnoid hemorrhage (SAH) often causes a prolongation of the corrected QT (QTc) interval during the acute phase. The aim of the present study was to examine independent risk factors for QTc prolongation in patients with SAH by means of multivariate analysis. Method We studied 100 patients who were admitted within 24 hours after onset of SAH. Standard 12-lead electrocardiography (ECG) was performed immediately after admission. QT intervals were measured from the ECG and were corrected for heart rate using the Bazett formula. We measured serum levels of sodium, potassium, calcium, adrenaline (epinephrine), noradrenaline (norepinephrine), dopamine, antidiuretic hormone, and glucose. Results The average QTc interval was 466 ± 46 ms. Patients were categorized into two groups based on the QTc interval, with a cutoff line of 470 ms. Univariate analyses showed significant relations between categories of QTc interval, and sex and serum concentrations of potassium, calcium, or glucose. Multivariate analyses showed that female sex and hypokalemia were independent risk factors for severe QTc prolongation. Hypokalemia (<3.5 mmol/l) was associated with a relative risk of 4.53 for severe QTc prolongation as compared with normokalemia, while the relative risk associated with female sex was 4.45 as compared with male sex. There was a significant inverse correlation between serum potassium levels and QTc intervals among female patients. Conclusion These findings suggest that female sex and hypokalemia are independent risk factors for severe QTc prolongation in patients with SAH. PMID:12793884

  16. A Morphometric Survey among Three Iranian Horse Breeds with Multivariate Analysis

    Directory of Open Access Journals (Sweden)

    M. Hosseini

    2016-12-01

    Full Text Available Three Iranian horse breeds, Turkoman, Caspian, and Kurdish, are the most important Iranian horse breeds which are well known in all around of the world because of their beauty, versatility, great stamina, and  intelligence. Phenotypic characterization was used to identify and document the diversity within and between distinct breeds, based on their observable attributes. Phenotypic characterization and body biometric in 23 traits were measured in 191 purebred horses belonging to three breeds, i.e. Turkoman (70 horses, Kurdish (77 horses, and Caspian (44 horses.  Caspian breed was  sampled from the Provinces of Alborz and Gilan. Kurdish breed was sampled from the Provinces of Kurdistan, Kermanshah, and Hamadan. Turkoman breed was sampled from the Provinces of Golestan, Markazi, and Isfahan. Multivariate analysis of variance (MANOVA was implemented. In addition, Canonical Discriminate Analysis (CDA, Principal Component Analysis (PCA, and Custer analysis were executed for assessing the relationship among the breeds. All statistical analysis was executed by SAS statistical program. The results of our investigation represented the breeds classification into 3 different classes (Caspian, Turkoman, and Kurdish based on different morphometrical traits. Caspian breed with smaller size in most variables was detached clearly from the others with more distance than Kurdish and Turkoman breeds. The result showed that the most variably trait for classification was Hind Hoof Length. Adaptation with different environments causes difference in morphology and difference among breeds. We can identify and classify domestic population using PCA, CDA, and cluster analysis.

  17. Spatial assessment of air quality patterns in Malaysia using multivariate analysis

    Science.gov (United States)

    Dominick, Doreena; Juahir, Hafizan; Latif, Mohd Talib; Zain, Sharifuddin M.; Aris, Ahmad Zaharin

    2012-12-01

    This study aims to investigate possible sources of air pollutants and the spatial patterns within the eight selected Malaysian air monitoring stations based on a two-year database (2008-2009). The multivariate analysis was applied on the dataset. It incorporated Hierarchical Agglomerative Cluster Analysis (HACA) to access the spatial patterns, Principal Component Analysis (PCA) to determine the major sources of the air pollution and Multiple Linear Regression (MLR) to assess the percentage contribution of each air pollutant. The HACA results grouped the eight monitoring stations into three different clusters, based on the characteristics of the air pollutants and meteorological parameters. The PCA analysis showed that the major sources of air pollution were emissions from motor vehicles, aircraft, industries and areas of high population density. The MLR analysis demonstrated that the main pollutant contributing to variability in the Air Pollutant Index (API) at all stations was particulate matter with a diameter of less than 10 μm (PM10). Further MLR analysis showed that the main air pollutant influencing the high concentration of PM10 was carbon monoxide (CO). This was due to combustion processes, particularly originating from motor vehicles. Meteorological factors such as ambient temperature, wind speed and humidity were also noted to influence the concentration of PM10.

  18. Variables predictive of voiding disfunction following aponeurotic sling surgery: multivariate analysis

    Directory of Open Access Journals (Sweden)

    Sílvio H.M. de Almeida

    2004-08-01

    Full Text Available INTRODUCTION: Aponeurotic sling surgeries can evolve with obstruction or voiding dysfunction in 5 to 20% of patients. There are few studies on factors that could possibly predispose to voiding difficulties or urinary retention. The objective of this work is to identify these potential clinical or urodynamic factors. MATERIALS AND METHODS: Records from 130 patients who underwent aponeurotic sling surgeries were reviewed. All patients underwent a throughout urodynamic study during pre-operative investigation. The variables studied were age above 65 years, previous pelvic surgeries, concomitant surgeries, post-voiding residue higher than 100 mL, vesical obstruction (according to Blaivas-Groutz nomogram and urinary flow under 12 mL/s. Post-voiding residue was assessed on the seventh post-operative day through vesical catheterization. Recovering of spontaneous voiding after 7 post-operative days or with a residue higher than 100 mL, was regarded as voiding dysfunction. Univariate analysis was performed with qui-square test and Fisher's exact test, and multivariate analysis was performed by logistic regression with alpha = 5%. RESULTS: Age in the studied group ranged from 41 to 83 years (mean 56.7 years, with 69 (53% patients having urethral hypermobility and 61 (47% having intrinsic urethral lesion. Normal voiding occurred in 97 (75.6 % women with 7 post-operative days. The only significant variable in the univariate (p = 0.014 and multivariate (p = 0.017 analysis was post-voiding residue higher than 100 mL. CONCLUSION: Pre-operative presence of a post-voiding residual urine higher than 100 mL was the only variable predictive of voiding dysfunction.

  19. Multivariate Meta-Analysis of Preference-Based Quality of Life Values in Coronary Heart Disease.

    Directory of Open Access Journals (Sweden)

    Jelena Stevanović

    Full Text Available There are numerous health-related quality of life (HRQol measurements used in coronary heart disease (CHD in the literature. However, only values assessed with preference-based instruments can be directly applied in a cost-utility analysis (CUA.To summarize and synthesize instrument-specific preference-based values in CHD and the underlying disease-subgroups, stable angina and post-acute coronary syndrome (post-ACS, for developed countries, while accounting for study-level characteristics, and within- and between-study correlation.A systematic review was conducted to identify studies reporting preference-based values in CHD. A multivariate meta-analysis was applied to synthesize the HRQoL values. Meta-regression analyses examined the effect of study level covariates age, publication year, prevalence of diabetes and gender.A total of 40 studies providing preference-based values were detected. Synthesized estimates of HRQoL in post-ACS ranged from 0.64 (Quality of Well-Being to 0.92 (EuroQol European"tariff", while in stable angina they ranged from 0.64 (Short form 6D to 0.89 (Standard Gamble. Similar findings were observed in estimates applying to general CHD. No significant improvement in model fit was found after adjusting for study-level covariates. Large between-study heterogeneity was observed in all the models investigated.The main finding of our study is the presence of large heterogeneity both within and between instrument-specific HRQoL values. Current economic models in CHD ignore this between-study heterogeneity. Multivariate meta-analysis can quantify this heterogeneity and offers the means for uncertainty around HRQoL values to be translated to uncertainty in CUAs.

  20. Detection of cervical lesions by multivariate analysis of diffuse reflectance spectra: a clinical study.

    Science.gov (United States)

    Prabitha, Vasumathi Gopala; Suchetha, Sambasivan; Jayanthi, Jayaraj Lalitha; Baiju, Kamalasanan Vijayakumary; Rema, Prabhakaran; Anuraj, Koyippurath; Mathews, Anita; Sebastian, Paul; Subhash, Narayanan

    2016-01-01

    Diffuse reflectance (DR) spectroscopy is a non-invasive, real-time, and cost-effective tool for early detection of malignant changes in squamous epithelial tissues. The present study aims to evaluate the diagnostic power of diffuse reflectance spectroscopy for non-invasive discrimination of cervical lesions in vivo. A clinical trial was carried out on 48 sites in 34 patients by recording DR spectra using a point-monitoring device with white light illumination. The acquired data were analyzed and classified using multivariate statistical analysis based on principal component analysis (PCA) and linear discriminant analysis (LDA). Diagnostic accuracies were validated using random number generators. The receiver operating characteristic (ROC) curves were plotted for evaluating the discriminating power of the proposed statistical technique. An algorithm was developed and used to classify non-diseased (normal) from diseased sites (abnormal) with a sensitivity of 72 % and specificity of 87 %. While low-grade squamous intraepithelial lesion (LSIL) could be discriminated from normal with a sensitivity of 56 % and specificity of 80 %, and high-grade squamous intraepithelial lesion (HSIL) from normal with a sensitivity of 89 % and specificity of 97 %, LSIL could be discriminated from HSIL with 100 % sensitivity and specificity. The areas under the ROC curves were 0.993 (95 % confidence interval (CI) 0.0 to 1) and 1 (95 % CI 1) for the discrimination of HSIL from normal and HSIL from LSIL, respectively. The results of the study show that DR spectroscopy could be used along with multivariate analytical techniques as a non-invasive technique to monitor cervical disease status in real time.

  1. Multivariate data analysis as a fast tool in evaluation of solid state phenomena.

    Science.gov (United States)

    Jørgensen, Anna Cecilia; Miroshnyk, Inna; Karjalainen, Milja; Jouppila, Kirsi; Siiriä, Simo; Antikainen, Osmo; Rantanen, Jukka

    2006-04-01

    A thorough understanding of solid state properties is of growing importance. It is often necessary to apply multiple techniques offering complementary information to fully understand the solid state behavior of a given compound and the relations between various polymorphic forms. The vast amount of information generated can be overwhelming and the need for more effective data analysis tools is well recognized. The aim of this study was to investigate the use of multivariate data analysis, in particular principal component analysis (PCA), for fast analysis of solid state information. The data sets analyzed covered dehydration phenomena of a set of hydrates followed by variable temperature X-ray powder diffractometry and Raman spectroscopy and the crystallization of amorphous lactose monitored by Raman spectroscopy. Identification of different transitional states upon the dehydration enabled the molecular level interpretation of the structural changes related to the loss of water, as well as interpretation of the phenomena related to the crystallization. The critical temperatures or critical time points were identified easily using the principal component analysis. The variables (diffraction angles or wavenumbers) that changed could be identified by the careful interpretation of the loadings plots. The PCA approach provides an effective tool for fast screening of solid state information.

  2. Assessment of soil quality parameters using multivariate analysis in the Rawal Lake watershed.

    Science.gov (United States)

    Firdous, Shahana; Begum, Shaheen; Yasmin, Azra

    2016-09-01

    Soil providing a wide array of ecosystem services is subjected to quality deterioration due to natural and anthropogenic factors. Most of the soils in Pakistan have poor status of available plant nutrients and cannot support optimum levels of crop productivity. The present study statistically analyzed ten soil quality parameters in five subwatersheds (Bari Imam, Chattar, Rumli, Shahdra, and Shahpur) of the Rawal Lake. Analysis of variance (ANOVA), cluster analysis (CA), and principal component analysis (PCA) were performed to evaluate correlation in soil quality parameters on spatiotemporal and vertical scales. Soil organic matter, electrical conductivity, nitrates, and sulfates were found to be lower than that required for good quality soil. Soil pH showed significant difference (p 0.75) and indicated that these were the most influential parameters of first factor or component. Cluster analysis separated five sampling sites into three statistically significant clusters: I (Shahdra-Bari Imam), II (Chattar), and III (Shahpur-Rumli). Among the five sites, Shahdra was found to have good quality soil followed by Bari Imam. The present study illustrated the usefulness of multivariate statistical approaches for the analysis and interpretation of complex datasets to understand variations in soil quality for effective watershed management.

  3. Enhancing multivariate singular spectrum analysis for phase synchronization: The role of observability

    Science.gov (United States)

    Portes, Leonardo L.; Aguirre, Luis A.

    2016-09-01

    Multivariate singular spectrum analysis (M-SSA) was recently adapted to study systems of coupled oscillators. It does not require an a priori definition for phase nor detailed knowledge of the individual oscillators, but it uses all the variables of each system. This aspect could be restrictive for practical applications, since usually just a few (sometimes only one) variables are measured. Based on dynamical systems and observability theories, we first show how to apply the M-SSA with only one variable and show the conditions to achieve good performance. Next, we provide numerical evidence that this single-variable approach enhances the explanatory power compared to the original M-SSA when computed with all the system variables. This could have important practical implications, as pointed out using benchmark oscillators.

  4. A Performance Analysis for UMTS Packet Switched Network Based on Multivariate KPIS

    CERN Document Server

    Ouyang, Ye; 10.5121/ijngn.2010.2107

    2010-01-01

    Mobile data services are penetrating mobile markets rapidly. The mobile industry relies heavily on data service to replace the traditional voice services with the evolution of the wireless technology and market. A reliable packet service network is critical to the mobile operators to maintain their core competence in data service market. Furthermore, mobile operators need to develop effective operational models to manage the varying mix of voice, data and video traffic on a single network. Application of statistical models could prove to be an effective approach. This paper first introduces the architecture of Universal Mobile Telecommunications System (UMTS) packet switched (PS) network and then applies multivariate statistical analysis to Key Performance Indicators (KPI) monitored from network entities in UMTS PS network to guide the long term capacity planning for the network. The approach proposed in this paper could be helpful to mobile operators in operating and maintaining their 3G packet switched netw...

  5. Comparison of multivariate data analysis strategies for high-content screening.

    Science.gov (United States)

    Kümmel, Anne; Selzer, Paul; Beibel, Martin; Gubler, Hanspeter; Parker, Christian N; Gabriel, Daniela

    2011-03-01

    High-content screening (HCS) is increasingly used in biomedical research generating multivariate, single-cell data sets. Before scoring a treatment, the complex data sets are processed (e.g., normalized, reduced to a lower dimensionality) to help extract valuable information. However, there has been no published comparison of the performance of these methods. This study comparatively evaluates unbiased approaches to reduce dimensionality as well as to summarize cell populations. To evaluate these different data-processing strategies, the prediction accuracies and the Z' factors of control compounds of a HCS cell cycle data set were monitored. As expected, dimension reduction led to a lower degree of discrimination between control samples. A high degree of classification accuracy was achieved when the cell population was summarized on well level using percentile values. As a conclusion, the generic data analysis pipeline described here enables a systematic review of alternative strategies to analyze multiparametric results from biological systems.

  6. Species delimitation in the fern genus Lemmaphyllum (Polypodiaceae) based on multivariate analysis of morphological variation

    Institute of Scientific and Technical Information of China (English)

    Xue-Ping WEI; Xian-Chun ZHANG

    2013-01-01

    The species delimitation of Lemmaphyllum,including the former segregated Lepidogrammitis in China,was considered as unresolved.Previous treatments accepted between one and 20 species.In the present study,multivariate analysis and maximum parsimony analyses were carried out on data from herbarium specimens of this genus by evaluating 24 quantitative characters and 13 qualitative characters.In total,558 specimens representing 11 previously accepted species and one variety in China were studied.As a result,three species and two varieties were recognized,Lemmaphyllum pyriforme,L.rostratum,L.carnosum,L.carnosum var.microphyllum,and L.carnosum var.drymoglossoides.Two new combinations and seven new synonyms were introduced.An identification key and distribution maps were provided.This study also elucidated the diagnostic value of two previously ignored characters,scales at the base of stipe and laminae hydathodes.

  7. A Performance Analysis for UMTS Packet Switched Network Based on Multivariate KPIS

    Directory of Open Access Journals (Sweden)

    Ye Ouyang

    2010-03-01

    Full Text Available Mobile data services are penetrating mobile markets rapidly. The mobile industry relies heavily on data service toreplace the traditional voice services with the evolution of the wireless technology and market. A reliable packetservice network is critical to the mobile operators to maintain their core competence in data service market.Furthermore, mobile operators need to develop effective operational models to manage the varying mix of voice,data and video traffic on a single network. Application of statistical models could prove to be an effective approach.This paper first introduces the architecture of Universal Mobile Telecommunications System (UMTS packetswitched (PS network and then applies multivariate statistical analysis to Key Performance Indicators (KPImonitored from network entities in UMTS PS network to guide the long term capacity planning for the network. Theapproach proposed in this paper could be helpful to mobile operators in operating and maintaining their 3G packetswitched networks for the long run.

  8. Prediction of chemical, physical and sensory data from process parameters for frozen cod using multivariate analysis

    DEFF Research Database (Denmark)

    Bechmann, Iben Ellegaard; Jensen, H.S.; Bøknæs, Niels

    1998-01-01

    Physical, chemical and sensory quality parameters were determined for 115 cod (Gadus morhua) samples stored under varying frozen storage conditions. Five different process parameters (period of frozen storage, frozen storage. temperature, place of catch, season for catching and state of rigor) were...... varied systematically at two levels. The data obtained were evaluated using the multivariate methods, principal component analysis (PCA) and partial least squares (PLS) regression. The PCA models were used to identify which process parameters were actually most important for the quality of the frozen cod....... PLS models that were able to predict the physical, chemical and sensory quality parameters from the process parameters of the frozen raw material were generated. The prediction abilities of the PLS models were good enough to give reasonable results even when the process parameters were characterised...

  9. Multivariate extreme value analysis of storm surges in SCS on peak over threshold method

    Directory of Open Access Journals (Sweden)

    Y. Luo

    2015-11-01

    Full Text Available We use a novel statistical approach-MGPD to analyze the joint probability distribution of storm surge events at two sites and present a warning method for storm surges at two adjacent positions in Beibu Gulf, using the sufficiently long field data on surge levels at two sites. The methodology also develops the procedure of application of MGPD, which includes joint threshold and Monte Carlo simulation, to handle multivariate extreme values analysis. By comparing the simulation result with analytic solution, it is shown that the relative error of the Monte Carlo simulation is less than 8.6 %. By running MGPD model based on long data at Beihai and Dongfang, the simulated potential surge results can be employed in storm surge warnings of Beihai and joint extreme water level predictions of two sites.

  10. A multivariate model for the meta-analysis of study level survival data at multiple times.

    Science.gov (United States)

    Jackson, Dan; Rollins, Katie; Coughlin, Patrick

    2014-09-01

    Motivated by our meta-analytic dataset involving survival rates after treatment for critical leg ischemia, we develop and apply a new multivariate model for the meta-analysis of study level survival data at multiple times. Our data set involves 50 studies that provide mortality rates at up to seven time points, which we model simultaneously, and we compare the results to those obtained from standard methodologies. Our method uses exact binomial within-study distributions and enforces the constraints that both the study specific and the overall mortality rates must not decrease over time. We directly model the probabilities of mortality at each time point, which are the quantities of primary clinical interest. We also present I(2) statistics that quantify the impact of the between-study heterogeneity, which is very considerable in our data set.

  11. Enhancing multivariate singular spectrum analysis for phase synchronization: The role of observability.

    Science.gov (United States)

    Portes, Leonardo L; Aguirre, Luis A

    2016-09-01

    Multivariate singular spectrum analysis (M-SSA) was recently adapted to study systems of coupled oscillators. It does not require an a priori definition for phase nor detailed knowledge of the individual oscillators, but it uses all the variables of each system. This aspect could be restrictive for practical applications, since usually just a few (sometimes only one) variables are measured. Based on dynamical systems and observability theories, we first show how to apply the M-SSA with only one variable and show the conditions to achieve good performance. Next, we provide numerical evidence that this single-variable approach enhances the explanatory power compared to the original M-SSA when computed with all the system variables. This could have important practical implications, as pointed out using benchmark oscillators.

  12. Building a Reduced Reference Video Quality Metric with Very Low Overhead Using Multivariate Data Analysis

    Directory of Open Access Journals (Sweden)

    Tobias Oelbaum

    2008-10-01

    Full Text Available In this contribution a reduced reference video quality metric for AVC/H.264 is proposed that needs only a very low overhead (not more than two bytes per sequence. This reduced reference metric uses well established algorithms to measure objective features of the video such as 'blur' or 'blocking'. Those measurements are then combined into a single measurement for the overall video quality. The weights of the single features and the combination of those are determined using methods provided by multivariate data analysis. The proposed metric is verified using a data set of AVC/H.264 encoded videos and the corresponding results of a carefully designed and conducted subjective evaluation. Results show that the proposed reduced reference metric not only outperforms standard PSNR but also two well known full reference metrics.

  13. Hyperspectral imaging with multivariate analysis for technological parameters prediction and classification of muscle foods: A review.

    Science.gov (United States)

    Cheng, Jun-Hu; Nicolai, Bart; Sun, Da-Wen

    2017-01-01

    Muscle foods are very important for a well-balanced daily diet. Due to their perishability and vulnerability, there is a need for quality and safety evaluation of such foods. Hyperspectral imaging (HSI) coupled with multivariate analysis is becoming increasingly popular for the non-destructive, non-invasive, and rapid determination of important quality attributes and the classification of muscle foods. This paper reviews recent advances of application of HSI for predicting some significant muscle foods parameters, including color, tenderness, firmness, springiness, water-holding capacity, drip loss and pH. In addition, algorithms for the rapid classification of muscle foods are also reported and discussed. It will be shown that this technology has great potential to replace traditional analytical methods for predicting various quality parameters and classifying muscle foods. Copyright © 2016 Elsevier Ltd. All rights reserved.

  14. Tract-Based Bayesian Multivariate Analysis of Mild Traumatic Brain Injury

    Science.gov (United States)

    Liu, Yongkang; Wang, Tianyao; Chen, Xiao; Zhang, Jianhua; Zhou, Guoxing; Wang, Zhongqiu; Chen, Rong

    2014-01-01

    Purpose. Detecting brain regions characterizing mild traumatic brain injury (mTBI) by combining Tract-Based Spatial Statistics (TBSS) and Graphical-model-based Multivariate Analysis (GAMMA). Materials and Methods. This study included 39 mTBI patients and 28 normal controls. Local research ethics committee approved this prospective study. Diffusion-tensor imaging was performed in mTBI patients within 7 days of injury. Skeletonized fractional anisotropy (FA) maps were generated by using TBSS. Brain regions characterizing mTBI were detected by GAMMA. Results. Two clusters of lower frontal white matter FA were present in mTBI patients. We constructed classifiers based on FA values in these two clusters to differentiate mTBI and controls. The mean accuracy, sensitivity, and specificity, across five different classifiers, were 0.80, 0.94, and 0.61, respectively. Conclusions. Combining TBSS and GAMMA can detect neuroimaging biomarkers characterizing mTBI. PMID:24711857

  15. A multivariate variational objective analysis-assimilation method. Part 1: Development of the basic model

    Science.gov (United States)

    Achtemeier, Gary L.; Ochs, Harry T., III

    1988-01-01

    The variational method of undetermined multipliers is used to derive a multivariate model for objective analysis. The model is intended for the assimilation of 3-D fields of rawinsonde height, temperature and wind, and mean level temperature observed by satellite into a dynamically consistent data set. Relative measurement errors are taken into account. The dynamic equations are the two nonlinear horizontal momentum equations, the hydrostatic equation, and an integrated continuity equation. The model Euler-Lagrange equations are eleven linear and/or nonlinear partial differential and/or algebraic equations. A cyclical solution sequence is described. Other model features include a nonlinear terrain-following vertical coordinate that eliminates truncation error in the pressure gradient terms of the horizontal momentum equations and easily accommodates satellite observed mean layer temperatures in the middle and upper troposphere. A projection of the pressure gradient onto equivalent pressure surfaces removes most of the adverse impacts of the lower coordinate surface on the variational adjustment.

  16. Factors associated with preterm birth in Cardiff, Wales. I. Univariable and multivariable analysis.

    Science.gov (United States)

    Meis, P J; Michielutte, R; Peters, T J; Wells, H B; Sands, R E; Coles, E C; Johns, K A

    1995-08-01

    Our purpose was to examine the associations of demographic, social, and medical factors with risk of preterm birth. By use of the Cardiff Births Survey, a large database of largely homogeneous (white) births in Wales, multivariable analysis by logistic regression examined the relative importance of risk variables associated with preterm birth. Significant independent associations with preterm birth were found (in decreasing order of magnitude) for late pregnancy bleeding, preeclampsia-proteinuria, low maternal weight, low maternal age, early pregnancy bleeding, history of previous stillbirth, smoking, high parity, low or high hemoglobin concentration, history of previous abortion, low social class, bacteriuria, and nulliparity. In this population demographic, social, and medical characteristics of the pregnancies showed significant associations with preterm birth.

  17. Multivariate analysis of sexual size dimorphism in local turkeys (Meleagris gallopavo) in Nigeria.

    Science.gov (United States)

    Ajayi, Oyeyemi O; Yakubu, Abdulmojeed; Jayeola, Oluwaseun O; Imumorin, Ikhide G; Takeet, Michael I; Ozoje, Michael O; Ikeobi, Christian O N; Peters, Sunday O

    2012-06-01

    Sexual size dimorphism is a key evolutionary feature that can lead to important biological insights. To improve methods of sexing live birds in the field, we assessed sexual size dimorphism in Nigerian local turkeys (Meleagris gallopavo) using multivariate techniques. Measurements were taken on 125 twenty-week-old birds reared under the intensive management system. The body parameters measured were body weight, body length, breast girth, thigh length, shank length, keel length, wing length and wing span. Univariate analysis revealed that toms (males) had significantly (P wing span were the most discriminating variables in separating the sexes. The single discriminant function obtained was able to correctly classify 100% of the birds into their source population. The results obtained from the present study could aid future management decisions, ecological studies and conservation of local turkeys in a developing economy.

  18. UV-vis absorption spectroscopy and multivariate analysis as a method to discriminate tequila

    Science.gov (United States)

    Barbosa-García, O.; Ramos-Ortíz, G.; Maldonado, J. L.; Pichardo-Molina, J. L.; Meneses-Nava, M. A.; Landgrave, J. E. A.; Cervantes-Martínez, J.

    2007-01-01

    Based on the UV-vis absorption spectra of commercially bottled tequilas, and with the aid of multivariate analysis, it is proved that different brands of white tequila can be identified from such spectra, and that 100% agave and mixed tequilas can be discriminated as well. Our study was done with 60 tequilas, 58 of them purchased at liquor stores in various Mexican cities, and two directly acquired from a distillery. All the tequilas were of the "white" type, that is, no aged spirits were considered. For the purposes of discrimination and quality control of tequilas, the spectroscopic method that we present here offers an attractive alternative to the traditional methods, like gas chromatography, which is expensive and time-consuming.

  19. Multivariate image analysis of laser-induced photothermal imaging used for detection of caries tooth

    Science.gov (United States)

    El-Sherif, Ashraf F.; Abdel Aziz, Wessam M.; El-Sharkawy, Yasser H.

    2010-08-01

    Time-resolved photothermal imaging has been investigated to characterize tooth for the purpose of discriminating between normal and caries areas of the hard tissue using thermal camera. Ultrasonic thermoelastic waves were generated in hard tissue by the absorption of fiber-coupled Q-switched Nd:YAG laser pulses operating at 1064 nm in conjunction with a laser-induced photothermal technique used to detect the thermal radiation waves for diagnosis of human tooth. The concepts behind the use of photo-thermal techniques for off-line detection of caries tooth features were presented by our group in earlier work. This paper illustrates the application of multivariate image analysis (MIA) techniques to detect the presence of caries tooth. MIA is used to rapidly detect the presence and quantity of common caries tooth features as they scanned by the high resolution color (RGB) thermal cameras. Multivariate principal component analysis is used to decompose the acquired three-channel tooth images into a two dimensional principal components (PC) space. Masking score point clusters in the score space and highlighting corresponding pixels in the image space of the two dominant PCs enables isolation of caries defect pixels based on contrast and color information. The technique provides a qualitative result that can be used for early stage caries tooth detection. The proposed technique can potentially be used on-line or real-time resolved to prescreen the existence of caries through vision based systems like real-time thermal camera. Experimental results on the large number of extracted teeth as well as one of the thermal image panoramas of the human teeth voltanteer are investigated and presented.

  20. Therapeutic factors related to irradiation in primary and metastatic liver cancer using multivariate analysis

    Energy Technology Data Exchange (ETDEWEB)

    Hatano, Kazuo (Chiba Univ. (Japan). School of Medicine)

    1990-06-01

    Between December 1973 and August 1987, 21 patients with primary liver cancer and 41 patients with metastatic liver cancer were treated with external irradiation, intra-arterial infusion chemotherapy and/or trans-arterial embolization (TAE) at the National Medical Center Hospital, the National South Kyushu Central Hospital and the National Kure Hospital. They were all inoperable cases. We diagnosed the tumor site and the involved area with many imagings and we decided the target volume. For primary liver cancer, the average survival period was 10.9 months, the 1-year survival rate was 28.6%, the 2-year survival rate was 14.3%, and the 3-year survival rate was 4.7%. Using multivariate analysis, stage, cancer reduction rate, Child classifiction and field size were valuable factors of the prognosis in the arterial infusion group. In the TAE group, cancer reduction rate was the most valuable factor. For metastatic liver cancer, the average survival period was 8.0 months, the 1-year survival rate was 19.5%, and the 5-year survival rate was 2.4%. Using multivariate analysis, pre-treatment K.P.S, distant metastasis, H, Child classification were valuable factors and pre-treatment K.P.S was most valuable factor. Although the indication of hepatic irradiation was generally thought to limit those cases which were Child A or B, H1 or H2 and the cases which have no distant metastasis, the cases which have their main portal tumor thrombus were also the indication of this therapy. (author).

  1. Abnormal Brain Areas Common to the Focal Epilepsies: Multivariate Pattern Analysis of fMRI.

    Science.gov (United States)

    Pedersen, Mangor; Curwood, Evan K; Vaughan, David N; Omidvarnia, Amir H; Jackson, Graeme D

    2016-04-01

    Individuals with focal epilepsy have heterogeneous sites of seizure origin. However, there may be brain regions that are common to most cases of intractable focal epilepsy. In this study, we aim to identify these using multivariate analysis of task-free functional MRI. Fourteen subjects with extratemporal focal epilepsy and 14 healthy controls were included in the study. Task-free functional MRI data were used to calculate voxel-wise regional connectivity with regional homogeneity (ReHo) and weighted degree centrality (DCw), in addition to regional activity using fraction of amplitude of low-frequency fluctuations (fALFF). Multivariate pattern analysis was applied to each of these metrics to discriminate brain areas that differed between focal epilepsy subjects and healthy controls. ReHo and DCw classified focal epilepsy subjects from healthy controls with high accuracy (89.3% and 75%, respectively). However, fALFF did not significantly classify patients from controls. Increased regional network activity in epilepsy subjects was seen in the ipsilateral piriform cortex, insula, and thalamus, in addition to the dorsal anterior cingulate cortex and lateral frontal cortices. Decreased regional connectivity was observed in the ventromedial prefrontal cortex, as well as lateral temporal cortices. Patients with extratemporal focal epilepsy have common areas of abnormality (ReHo and DCw measures), including the ipsilateral piriform cortex, temporal neocortex, and ventromedial prefrontal cortex. ReHo shows additional increase in the "salience network" that includes anterior insula and anterior cingulate cortex. DCw showed additional effects in the ipsilateral thalamus and striatum. These brain areas may represent key regional network properties underlying focal epilepsy.

  2. Multivariate statistical analysis of radioactive variables in two phosphate ores from Sudan.

    Science.gov (United States)

    Adam, Abdel Majid A; Eltayeb, Mohamed Ahmed H

    2012-05-01

    Multivariate statistical techniques are efficient ways to display complex relationships among many objects. An attempt was made to study the radioactive data in two types of Sudanese phosphate deposits; Kurun and Uro phosphate, using several multivariate statistical methods. Pearson correlation coefficient revealed that a U-238 distribution in Kurun phosphate is controlled by the variation of K-40 concentration, whereas in Uro phosphate it is controlled by the variation of U-235 and U-234 concentration. Histograms and normal Q-Q plots clearly show that the radioactive variables did not follow a normal distribution. This non-normality feature observed may be attributed to complicating influence of geological factors. The principal components analysis (PCA) gives a model of five components for representing the acquired data from Kurun phosphate, where 89.5% of the total variance is explained. A model of four components was sufficient to represent the acquired data from Uro phosphate, where 87.5% of the total data variance is explained. The hierarchical cluster analysis (HCA) indicates that U-238 behaves in the same manner in the two types of phosphates; it associated with a group of four radionuclides; U-234, Po-210, Ra-226, Th-230, which the most abundant radionuclides, and all belong to the uranium-238 decay series. Two parameters have been adapted for the direct differentiate between the two phosphates. Firstly, U-238 in Uro phosphate have shown higher degree of mobility (CV% = 82.6) than that in Kurun phosphate (CV% = 64.7), and secondly, the activity ratio of Th-230/Th-232 in Uro phosphate is nine times than that in Kurun phosphate.

  3. Multivariate Analysis of Risk Factors of Cerebral Infarction in 439 Patients Undergoing Thoracic Endovascular Aneurysm Repair.

    Science.gov (United States)

    Kanaoka, Yuji; Ohki, Takao; Maeda, Koji; Baba, Takeshi; Fujita, Tetsuji

    2016-04-01

    The aim of the study is to identify the potential risk factors of cerebral infarction associated with thoracic endovascular aneurysm repair (TEVAR). TEVAR was developed as a less invasive surgical alternative to conventional open repair for thoracic aortic aneurysm treatment. However, outcomes following TEVAR of aortic and distal arch aneurysms remain suboptimal. Cerebral infarction is a major concern during the perioperative period. We included 439 patients who underwent TEVAR of aortic aneurysms at a high-volume teaching hospital between July 2006 and June 2013. Univariate and multivariate logistic regression analyses were performed to identify perioperative cerebral infarction risk factors. Four patients (0.9%) died within 30 days of TEVAR; 17 (3.9%) developed cerebral infarction. In univariate analysis, history of ischemic heart disease and cerebral infarction and concomitant cerebrovascular disease were significantly associated with cerebral infarction. "Shaggy aorta" presence, left subclavian artery coverage, carotid artery debranching, and pull-through wire use were identified as independent risk factors of cerebral infarction. In multivariate analysis, history of ischemic heart disease (odds ratio [OR] 6.49, P = 0.046) and cerebral infarction (OR 43.74, P = 0.031), "shaggy aorta" (OR 30.32, P < 0.001), pull-through wire use during surgery (OR 7.196, P = 0.014), and intraoperative blood loss ≥800 mL (OR 24.31, P = 0.017) were found to be independent risk factors of cerebral infarction. This study identified patient- and procedure-related risk factors of cerebral infarction following TEVAR. These results indicate that patient outcomes could be improved through the identification and management of procedure-related risk factors.

  4. Incidental durotomy during spinal surgery: a multivariate analysis for risk factors.

    Science.gov (United States)

    Du, Jerry Y; Aichmair, Alexander; Kueper, Janina; Lam, Cyrena; Nguyen, Joseph T; Cammisa, Frank P; Lebl, Darren R

    2014-10-15

    Multivariate analysis. The purpose of this study was to investigate risk factors for incidental durotomy (ID) in modern spine surgery techniques. ID, a relatively common complication of spine surgery, has been associated with postoperative complications such as durocutaneous fistulas, pseudomeningoceles, and arachnoiditis. Revision surgery may be necessary if the dural tear is not recognized and repaired during the initial procedure. ID was prospectively documented in patients who underwent spine surgery at a single institution during a 2-year period (n=4822). Patients with ID (n=182) from lumbar or thoracolumbar cases were matched 1:1 to a control cohort without ID. Demographic, diagnostic, and surgical procedure data were retrospectively collected and analyzed. Multivariate analysis identified revision spine surgery (adjusted odds ratio [aOR]: 4.78, 95% confidence interval [CI]: 2.84-8.06, P<0.01), laminectomy (aOR: 3.82, 95% CI: 2.02-7.22, P<0.01), and older age (aOR: 1.03, 95% CI: 1.01-1.04, P<0.01) as independent risk factors for ID. Fusion (aOR: 0.59, 95% CI: 0.35-0.99, P=0.04), foraminectomy, (aOR: 0.42, 95% CI: 0.25-0.69, P<0.01), and lateral approach (aOR: 0.29, 95% CI: 0.14-0.61, P<0.01) were independent protective factors. Prior spine surgery, laminectomy, and older age were significant independent risk factors for ID. The recently developed lateral approach to interbody fusion was identified as a significant protective factor for ID, along with fusion and foraminectomy. These findings may help guide future surgical decisions regarding ID and aid in the patient informed-consent process. 3.

  5. Pain in diagnostic hysteroscopy: a multivariate analysis after a randomized, controlled trial.

    Science.gov (United States)

    Mazzon, Ivan; Favilli, Alessandro; Grasso, Mario; Horvath, Stefano; Bini, Vittorio; Di Renzo, Gian Carlo; Gerli, Sandro

    2014-11-01

    To study which variables are able to influence women's experience of pain during diagnostic hysteroscopy. Multivariate analysis (phase II) after a randomized, controlled trial (phase I). Endoscopic gynecologic center. In phase I, 392 patients were analyzed. Group A: 197 women with carbon dioxide (CO2); group B: 195 women with normal saline. In phase II, 392 patients were assigned to two different groups according to their pain experience as measured by a visual analogue scale (VAS): group VAS>3 (170 patients); group VAS≤3 (222 patients). Free-anesthesia diagnostic hysteroscopy performed using CO2 or normal saline as distension media. Procedure time, VAS score, image quality, and side effects during and after diagnostic hysteroscopy. In phase I the median pain score in group A was 2, whereas in group B it was 3. In phase II the duration of the procedure, nulliparity, and the use of normal saline were significantly correlated with VAS>3. A higher presence of cervical synechiae was observed in the group VAS>3. The multivariate analysis revealed an inverse correlation between parity and a VAS>3, whereas the use of normal saline, the presence of synechiae in the cervical canal, and the duration of the hysteroscopy were all directly correlated to a VAS score>3. Pain in hysteroscopy is significantly related to the presence of cervical synechiae, to the duration of the procedure, and to the use of normal saline; conversely, parity seems to have a protective role. NCT01873391. Copyright © 2014 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

  6. Inhibitory kinetics and mechanism of kaempferol on α-glucosidase.

    Science.gov (United States)

    Peng, Xi; Zhang, Guowen; Liao, Yijing; Gong, Deming

    2016-01-01

    α-Glucosidase is a therapeutic target for diabetes mellitus, and α-glucosidase inhibitors play a vital role in the treatments for the disease. As a kind of potentially safer α-glucosidase inhibitor, flavonoids have attached much attention currently. In this study, kaempferol was found to show a notable inhibition activity on α-glucosidase in a mixed-type manner with IC50 value of (1.16 ± 0.04) × 10(-5) mol L(-1). Analyses of fluorescence, circular dichroism and Fourier transform infrared spectra indicated that kaempferol bound to α-glucosidase with high affinity which was mainly driven by hydrogen bonds and van der Waals forces, and this binding resulted in conformational alteration of α-glucosidase. Further molecular docking study validated the experimental results. It was proposed that kaempferol may interact with some amino acid residues located within the active site of α-glucosidase, occupying the catalytic center of the enzyme to avoid the entrance of p-nitrophenyl-α-D-glucopyranoside and ultimately inhibiting the enzyme activity.

  7. Extracting coal ash content from laser-induced breakdown spectroscopy (LIBS) spectra by multivariate analysis.

    Science.gov (United States)

    Yao, Shunchun; Lu, Jidong; Dong, Meirong; Chen, Kai; Li, Junyan; Li, Jun

    2011-10-01

    Laser-induced breakdown spectroscopy (LIBS) combined with partial least squares (PLS) analysis has been applied for the quantitative analysis of the ash content of coal in this paper. The multivariate analysis method was employed to extract coal ash content information from LIBS spectra rather than from the concentrations of the main ash-forming elements. In order to construct a rigorous partial least squares regression model and reduce the calculation time, different spectral range data were used to construct partial least squares regression models, and then the performances of these models were compared in terms of the correlation coefficients of calibration and validation and the root mean square errors of calibration and cross-validation. Afterwards, the prediction accuracy, reproducibility, and the limit of detection of the partial least squares regression model were validated with independent laser-induced breakdown spectroscopy measurements of four unknown samples. The results show that a good agreement is observed between the ash content provided by thermo-gravimetric analyzer and the LIBS measurements coupled to the PLS regression model for the unknown samples. The feasibility of extracting coal ash content from LIBS spectra is approved. It is also confirmed that this technique has good potential for quantitative analysis of the ash content of coal.

  8. Multivariate qualitative analysis of banned additives in food safety using surface enhanced Raman scattering spectroscopy.

    Science.gov (United States)

    He, Shixuan; Xie, Wanyi; Zhang, Wei; Zhang, Liqun; Wang, Yunxia; Liu, Xiaoling; Liu, Yulong; Du, Chunlei

    2015-02-25

    A novel strategy which combines iteratively cubic spline fitting baseline correction method with discriminant partial least squares qualitative analysis is employed to analyze the surface enhanced Raman scattering (SERS) spectroscopy of banned food additives, such as Sudan I dye and Rhodamine B in food, Malachite green residues in aquaculture fish. Multivariate qualitative analysis methods, using the combination of spectra preprocessing iteratively cubic spline fitting (ICSF) baseline correction with principal component analysis (PCA) and discriminant partial least squares (DPLS) classification respectively, are applied to investigate the effectiveness of SERS spectroscopy for predicting the class assignments of unknown banned food additives. PCA cannot be used to predict the class assignments of unknown samples. However, the DPLS classification can discriminate the class assignment of unknown banned additives using the information of differences in relative intensities. The results demonstrate that SERS spectroscopy combined with ICSF baseline correction method and exploratory analysis methodology DPLS classification can be potentially used for distinguishing the banned food additives in field of food safety.

  9. Multivariate analysis of chromatographic retention data as a supplementary means for grouping structurally related compounds.

    Science.gov (United States)

    Fasoula, S; Zisi, Ch; Sampsonidis, I; Virgiliou, Ch; Theodoridis, G; Gika, H; Nikitas, P; Pappa-Louisi, A

    2015-03-27

    In the present study a series of 45 metabolite standards belonging to four chemically similar metabolite classes (sugars, amino acids, nucleosides and nucleobases, and amines) was subjected to LC analysis on three HILIC columns under 21 different gradient conditions with the aim to explore whether the retention properties of these analytes are determined from the chemical group they belong. Two multivariate techniques, principal component analysis (PCA) and discriminant analysis (DA), were used for statistical evaluation of the chromatographic data and extraction similarities between chemically related compounds. The total variance explained by the first two principal components of PCA was found to be about 98%, whereas both statistical analyses indicated that all analytes are successfully grouped in four clusters of chemical structure based on the retention obtained in four or at least three chromatographic runs, which, however should be performed on two different HILIC columns. Moreover, leave-one-out cross-validation of the above retention data set showed that the chemical group in which an analyte belongs can be 95.6% correctly predicted when the analyte is subjected to LC analysis under the same four or three experimental conditions as the all set of analytes was run beforehand. That, in turn, may assist with disambiguation of analyte identification in complex biological extracts.

  10. Multivariate qualitative analysis of banned additives in food safety using surface enhanced Raman scattering spectroscopy

    Science.gov (United States)

    He, Shixuan; Xie, Wanyi; Zhang, Wei; Zhang, Liqun; Wang, Yunxia; Liu, Xiaoling; Liu, Yulong; Du, Chunlei

    2015-02-01

    A novel strategy which combines iteratively cubic spline fitting baseline correction method with discriminant partial least squares qualitative analysis is employed to analyze the surface enhanced Raman scattering (SERS) spectroscopy of banned food additives, such as Sudan I dye and Rhodamine B in food, Malachite green residues in aquaculture fish. Multivariate qualitative analysis methods, using the combination of spectra preprocessing iteratively cubic spline fitting (ICSF) baseline correction with principal component analysis (PCA) and discriminant partial least squares (DPLS) classification respectively, are applied to investigate the effectiveness of SERS spectroscopy for predicting the class assignments of unknown banned food additives. PCA cannot be used to predict the class assignments of unknown samples. However, the DPLS classification can discriminate the class assignment of unknown banned additives using the information of differences in relative intensities. The results demonstrate that SERS spectroscopy combined with ICSF baseline correction method and exploratory analysis methodology DPLS classification can be potentially used for distinguishing the banned food additives in field of food safety.

  11. [Near infrared spectroscopy and multivariate statistical process analysis for real-time monitoring of production process].

    Science.gov (United States)

    Wang, Yi; Ma, Xiang; Wen, Ya-Dong; Zou, Quan; Wang, Jun; Tu, Jia-Run; Cai, Wen-Sheng; Shao, Xue-Guang

    2013-05-01

    Near infrared diffusive reflectance spectroscopy has been applied in on-site or on-line analysis due to its characteristics of fastness, non-destruction and the feasibility for real complex sample analysis. The present work reported a real-time monitoring method for industrial production by using near infrared spectroscopic technique and multivariate statistical process analysis. In the method, the real-time near infrared spectra of the materials are collected on the production line, and then the evaluation of the production process can be achieved by a statistic Hotelling T2 calculated with the established model. In this work, principal component analysis (PCA) is adopted for building the model, and the statistic is calculated by projecting the real-time spectra onto the PCA model. With an application of the method in a practical production, it was demonstrated that a real-time evaluation of the variations in the production can be realized by investigating the changes in the statistic, and the comparison of the products in different batches can be achieved by further statistics of the statistic. Therefore, the proposed method may provide a practical way for quality insurance of production processes.

  12. Determination of volatile organic compounds pollution sources in malaysian drinking water using multivariate analysis.

    Science.gov (United States)

    Soh, Shiau-Chian; Abdullah, Md Pauzi

    2007-01-01

    A field investigation was conducted at all water treatment plants throughout 11 states and Federal Territory in Peninsular Malaysia. The sampling points in this study include treatment plant operation, service reservoir outlet and auxiliary outlet point at the water pipelines. Analysis was performed by solid phase micro-extraction technique with a 100 microm polydimethylsiloxane fibre using gas chromatography with mass spectrometry detection to analyse 54 volatile organic compounds (VOCs) of different chemical families in drinking water. The concentration of VOCs ranged from undetectable to 230.2 microg/l. Among all of the VOCs species, chloroform has the highest concentration and was detected in all drinking water samples. Average concentrations of total trihalomethanes (THMs) were almost similar among all states which were in the range of 28.4--33.0 microg/l. Apart from THMs, other abundant compounds detected were cis and trans-1,2-dichloroethylene, trichloroethylene, 1,2-dibromoethane, benzene, toluene, ethylbenzene, chlorobenzene, 1,4-dichlorobenzene and 1,2-dichloro - benzene. Principal component analysis (PCA) with the aid of varimax rotation, and parallel factor analysis (PARAFAC) method were used to statistically verify the correlation between VOCs and the source of pollution. The multivariate analysis pointed out that the maintenance of auxiliary pipelines in the distribution systems is vital as it can become significant point source pollution to Malaysian drinking water.

  13. CoSMoMVPA: multi-modal multivariate pattern analysis of neuroimaging datain Matlab / GNU Octave

    Directory of Open Access Journals (Sweden)

    Nikolaas N Oosterhof

    2016-07-01

    Full Text Available Recent years have seen an increase in the popularity of multivariate pattern (MVP analysis of functional magnetic resonance (fMRI data, and, to a much lesser extent, magneto- and electro-encephalography (M/EEG data. We present CoSMoMVPA, a lightweight MVPA (MVP analysis toolbox implemented in the intersection of the Matlab and GNU Octave languages, that treats both fMRI and M/EEG data as first-class citizens.CoSMoMVPA supports all state-of-the-art MVP analysis techniques, including searchlight analyses, classification, correlations, representational similarity analysis, and the time generalization method. These can be used to address both data-driven and hypothesis-driven questions about neural organization and representations, both within and across: space, time, frequency bands, neuroimaging modalities, individuals, and species.It uses a uniform data representation of fMRI data in the volume or on the surface, and of M/EEG data at the sensor and source level. Through various external toolboxes, it directly supports reading and writing a variety of fMRI and M/EEG neuroimaging formats, and, where applicable, can convert between them. As a result, it can be integrated readily in existing pipelines and used with existing preprocessed datasets. CoSMoMVPA overloads the traditional volumetric searchlight concept to support neighborhoods for M/EEG and surface-based fMRI data, which supports localization of multivariate effects of interest across space, time, and frequency dimensions. CoSMoMVPA also provides a generalized approach to multiple comparison correction across these dimensions using Threshold-Free Cluster Enhancement with state-of-the-art clustering and permutation techniques.CoSMoMVPA is highly modular and uses abstractions to provide a uniform interface for a variety of MVP measures. Typical analyses require a few lines of code, making it accessible to beginner users. At the same time, expert programmers can easily extend its functionality

  14. Multivariate Analysis of the Factors Associated With Sexual Intercourse, Marriage, and Paternity of Hypospadias Patients.

    Science.gov (United States)

    Kanematsu, Akihiro; Higuchi, Yoshihide; Tanaka, Shiro; Hashimoto, Takahiko; Nojima, Michio; Yamamoto, Shingo

    2016-10-01

    employment (P = .020 and .026, respectively), and paternity was associated with the absence of additional surgery after completion of the initial repair (P = .013 by multivariate analysis). There was scant overlap of factors associated with the three events. The present findings provide reference information for surgeons and parents regarding future sexual and marriage experiences of children treated for hypospadias. Copyright © 2016 International Society for Sexual Medicine. Published by Elsevier Inc. All rights reserved.

  15. Multivariate analysis of cell culture bioprocess data--lactate consumption as process indicator.

    Science.gov (United States)

    Le, Huong; Kabbur, Santosh; Pollastrini, Luciano; Sun, Ziran; Mills, Keri; Johnson, Kevin; Karypis, George; Hu, Wei-Shou

    2012-12-31

    Multivariate analysis of cell culture bioprocess data has the potential of unveiling hidden process characteristics and providing new insights into factors affecting process performance. This study investigated the time-series data of 134 process parameters acquired throughout the inoculum train and the production bioreactors of 243 runs at the Genentech's Vacaville manufacturing facility. Two multivariate methods, kernel-based support vector regression (SVR) and partial least square regression (PLSR), were used to predict the final antibody concentration and the final lactate concentration. Both product titer and the final lactate level were shown to be predicted accurately when data from the early stages of the production scale were employed. Using only process data from the inoculum train, the prediction accuracy of the final process outcome was lower; the results nevertheless suggested that the history of the culture may exert significant influence on the final process outcome. The parameters contributing most significantly to the prediction accuracy were related to lactate metabolism and cell viability in both the production scale and the inoculum train. Lactate consumption, which occurred rather independently of the residual glucose and lactate concentrations, was shown to be a prominent factor in determining the final outcome of production-scale cultures. The results suggest possible opportunities to intervene in metabolism, steering it towards the type with a strong propensity towards high productivity. Such intervention could occur in the inoculum stage or in the early stage of the production-scale reactors. Overall, this study presents pattern recognition as an important process analytical technology (PAT). Furthermore, the high correlation between lactate consumption and high productivity can provide a guide to apply quality by design (QbD) principles to enhance process robustness.

  16. Biological data analysis as an information theory problem: multivariable dependence measures and the shadows algorithm.

    Science.gov (United States)

    Sakhanenko, Nikita A; Galas, David J

    2015-11-01

    Information theory is valuable in multiple-variable analysis for being model-free and nonparametric, and for the modest sensitivity to undersampling. We previously introduced a general approach to finding multiple dependencies that provides accurate measures of levels of dependency for subsets of variables in a data set, which is significantly nonzero only if the subset of variables is collectively dependent. This is useful, however, only if we can avoid a combinatorial explosion of calculations for increasing numbers of variables.  The proposed dependence measure for a subset of variables, τ, differential interaction information, Δ(τ), has the property that for subsets of τ some of the factors of Δ(τ) are significantly nonzero, when the full dependence includes more variables. We use this property to suppress the combinatorial explosion by following the "shadows" of multivariable dependency on smaller subsets. Rather than calculating the marginal entropies of all subsets at each degree level, we need to consider only calculations for subsets of variables with appropriate "shadows." The number of calculations for n variables at a degree level of d grows therefore, at a much smaller rate than the binomial coefficient (n, d), but depends on the parameters of the "shadows" calculation. This approach, avoiding a combinatorial explosion, enables the use of our multivariable measures on very large data sets. We demonstrate this method on simulated data sets, and characterize the effects of noise and sample numbers. In addition, we analyze a data set of a few thousand mutant yeast strains interacting with a few thousand chemical compounds.

  17. Multivariate Gradient Analysis for Evaluating and Visualizing a Learning System Platform for Computer Programming

    Directory of Open Access Journals (Sweden)

    Richard Mather

    2015-02-01

    Full Text Available This paper explores the application of canonical gradient analysis to evaluate and visualize student performance and acceptance of a learning system platform. The subject of evaluation is a first year BSc module for computer programming. This uses ‘Ceebot’, an animated and immersive game-like development environment. Multivariate ordination approaches are widely used in ecology to explore species distribution along environmental gradients. Environmental factors are represented here by three ‘assessment’ gradients; one for the overall module mark and two independent tests of programming knowledge and skill. Response data included Likert expressions for behavioral, acceptance and opinion traits. Behavioral characteristics (such as attendance, collaboration and independent study were regarded to be indicative of learning activity. Acceptance and opinion factors (such as perceived enjoyment and effectiveness of Ceebot were treated as expressions of motivation to engage with the learning environment. Ordination diagrams and summary statistics for canonical analyses suggested that logbook grades (the basis for module assessment and code understanding were weakly correlated. Thus strong module performance was not a reliable predictor of programming ability. The three assessment indices were correlated with behaviors of independent study and peer collaboration, but were only weakly associated with attendance. Results were useful for informing teaching practice and suggested: (1 realigning assessments to more fully capture code-level skills (important in the workplace; (2 re-evaluating attendance-based elements of module design; and (3 the overall merit of multivariate canonical gradient approaches for evaluating and visualizing the effectiveness of a learning system platform.

  18. ATR microspectroscopy with multivariate analysis segregates grades of exfoliative cervical cytology.

    Science.gov (United States)

    Walsh, Michael J; Singh, Maneesh N; Pollock, Hubert M; Cooper, Leanne J; German, Matthew J; Stringfellow, Helen F; Fullwood, Nigel J; Paraskevaidis, Evangelos; Martin-Hirsch, Pierre L; Martin, Francis L

    2007-01-05

    Although cervical cancer screening in the UK has led to reductions in the incidence of invasive disease, this programme remains flawed. We set out to examine the potential of infrared (IR) microspectroscopy to allow the profiling of cellular biochemical constituents associated with disease progression. Attenuated total reflection-Fourier Transform IR (ATR) microspectroscopy was employed to interrogate spectral differences between samples of exfoliative cervical cytology collected into liquid based cytology (LBC). These were histologically characterised as normal (n = 5), low-grade (n = 5), high-grade (n = 5) or severe dyskaryosis (? carcinoma) (n = 5). Examination of resultant spectra was coupled with principal component analysis (PCA) and subsequent linear discriminant analysis (LDA). The interrogation of LBC samples using ATR microspectroscopy with PCA-LDA facilitated the discrimination of different categories of exfoliative cytology and allowed the identification of potential biomarkers of abnormality; these occurred prominently in the IR spectral region 1200 cm(-1) - 950 cm(-1) consisting of carbohydrates, phosphate, and glycogen. Shifts in the centroids of amide I (approximately 1650 cm(-1)) and II (approximately 1530 cm(-1)) absorbance bands, indicating conformational changes to the secondary structure of intracellular proteins and associated with increasing disease progression, were also noted. This work demonstrates the potential of ATR microspectroscopy coupled with multivariate analysis to be an objective alternative to routine cytology.

  19. Classification of emotions by multivariate analysis and individual differences of nuclear power plant operators` emotion

    Energy Technology Data Exchange (ETDEWEB)

    Hasegawa, Naoko; Yoshimura, Seiichi [Central Research Inst. of Electric Power Industry, Tokyo (Japan)

    1999-03-01

    The purpose of this study is the development of a simulation model which expresses operators` emotion under plant emergency. This report shows the classification of emotions by multivariate analysis and investigation results conducted to clarify individual differences of activated emotion influenced by personal traits. Although a former investigation was conducted to classify emotions into five basic emotions proposed by Johnson-Laird, the basic emotions was not based on real data. For the development of more realistic and accurate simulation model, it is necessary to recognize basic emotion and to classify emotions into them. As a result of analysis by qualification method 3 and cluster analysis, four basic clusters were clarified, i.e., Emotion expressed towards objects, Emotion affected by objects, Pleasant emotion, and Surprise. Moreover, 51 emotions were ranked in the order according to their similarities in each cluster. An investigation was conducted to clarify individual differences in emotion process using 87 plant operators. The results showed the differences of emotion depending on the existence of operators` foresight, cognitive style, experience in operation, and consciousness of attribution to an operating team. For example, operators with low self-efficacy, short experience or low consciousness of attribution to a team, feel more intensive emotion under plant emergency and more affected by severe plant conditions. The model which can express individual differences will be developed utilizing and converting these data hereafter. (author)

  20. Research Update: Spatially resolved mapping of electronic structure on atomic level by multivariate statistical analysis

    Energy Technology Data Exchange (ETDEWEB)

    Belianinov, Alex, E-mail: belianinova@ornl.gov; Ganesh, Panchapakesan; Lin, Wenzhi; Jesse, Stephen; Pan, Minghu; Kalinin, Sergei V. [Oak Ridge National Laboratory, Institute for Functional Imaging of Materials, Center for Nanophase Material Science, Oak Ridge, Tennessee 37922 (United States); Sales, Brian C.; Sefat, Athena S. [Oak Ridge National Laboratory, Materials Science and Technology Division, Oak Ridge, Tennessee 37922 (United States)

    2014-12-01

    Atomic level spatial variability of electronic structure in Fe-based superconductor FeTe{sub 0.55}Se{sub 0.45} (T{sub c} = 15 K) is explored using current-imaging tunneling-spectroscopy. Multivariate statistical analysis of the data differentiates regions of dissimilar electronic behavior that can be identified with the segregation of chalcogen atoms, as well as boundaries between terminations and near neighbor interactions. Subsequent clustering analysis allows identification of the spatial localization of these dissimilar regions. Similar statistical analysis of modeled calculated density of states of chemically inhomogeneous FeTe{sub 1−x}Se{sub x} structures further confirms that the two types of chalcogens, i.e., Te and Se, can be identified by their electronic signature and differentiated by their local chemical environment. This approach allows detailed chemical discrimination of the scanning tunneling microscopy data including separation of atomic identities, proximity, and local configuration effects and can be universally applicable to chemically and electronically inhomogeneous surfaces.

  1. [Phytoplankton assemblages and their relation to environmental factors by multivariate statistic analysis in Bohai Bay].

    Science.gov (United States)

    Zhou, Ran; Peng, Shi-Tao; Qin, Xue-Bo; Shi, Hong-Hua; Ding, De-Wen

    2013-03-01

    A detailed field survey of hydrological, chemical and biological resources was conducted in the Bohai Bay in spring and summer 2007. The distributions of phytoplankton and their relations to environmental factors were investigated with multivariate analysis techniques. Totally 17 and 23 taxa were identified in spring and summer, respectively. The abundance of phytoplankton in spring was 115 x 10(4) cells x m(-3), which was significantly higher than that in summer (3.1 x 10(4) cells x m(-3)). Characteristics of phytoplankton assemblages in the two seasons were identified using principal component analysis (PCA), while redundancy analysis (RDA) was used to examine the environmental variables that may explain the patterns of variation of the phytoplankton community. Based on PCA results, in the spring, the phytoplankton was mainly distributed in the center and northern water zone, where the nitrate nitrogen concentration was higher. However, in summer, phytoplankton was found distributed in all zones of Bohai Bay, while the dominant species was mainly distributed in the estuary. RDA indicated that the key environmental factors that influenced phytoplankton assemblages in the spring were nitrate nitrogen (NO3(-) -N), nitrite nitrogen (NO2(-) -N) and soluble reactive phosphorus (SRP), while ammonium nitrogen (NH4(+) -N) and water temperature (WT) played key roles in summer.

  2. Urban-rural gradient detection using multivariate spatial analysis and landscape metrics

    Directory of Open Access Journals (Sweden)

    Marco Vizzari

    2013-09-01

    Full Text Available The gradient approach allows for an innovative representation of landscape composition and configuration not presupposing spatial discontinuities typical of the conventional methods of analysis. Also the urban-rural dichotomy can be better understood through a continuous landscape gradient whose characterization changes accordingly to natural and anthropic variables taken into account and to the spatio-temporal scale adopted for the study. The research was aimed at the analysis of an urban-rural gradient within a study area located in central Italy, using spatial indicators associated with urbanization, agriculture and natural elements. A multivariate spatial analysis (MSA of such indicators enabled the identification of urban, agricultural and natural dominated areas, as well as specific landscape transitions where the most relevant relationships between agriculture and other landscape components were detected. Landscapes derived from MSA were studied by a set of key landscape pattern metrics within a framework oriented to the structural characterization of the whole urban-rural gradient. The results showed two distinct sub-gradients: one urban-agricultural and one agricultural-natural, both characterized by different fringe areas. This application highlighted how the proposed methodology can represent a reliable approach supporting modern landscape planning and management.

  3. Population structure of the Korean gizzard shad, Konosirus punctatus (Clupeiformes, Clupeidae) using multivariate morphometric analysis

    Science.gov (United States)

    Myoung, Se Hun; Kim, Jin-Koo

    2016-03-01

    The gizzard shad, Konosirus punctatus, is one of the most important fish species in Korea, China, Japan and Taiwan, and therefore the implementation of an appropriate population structure analysis is both necessary and fitting. In order to clarify the current distribution range for the two lineages of the Korean gizzard shad (Myoung and Kim 2014), we conducted a multivariate morphometric analysis by locality and lineage. We analyzed 17 morphometric and 5 meristic characters of 173 individuals, which were sampled from eight localities in the East Sea, the Yellow Sea and the Korean Strait. Unlike population genetics studies, the canonical discriminant analysis (CDA) results showed that the two morphotypes were clearly segregated by the center value "0" of CAN1, of which morphotype A occurred from the Yellow Sea to the western Korean Strait with negative values, and morphotype B occurred from the East Sea to the eastern Korean Strait with positive values even though there exists an admixture zone in the eastern Korean Strait. Further studies using more sensitive markers such as microsatellite DNA are required in order to define the true relationship between the two lineages.

  4. Development of a scale down cell culture model using multivariate analysis as a qualification tool.

    Science.gov (United States)

    Tsang, Valerie Liu; Wang, Angela X; Yusuf-Makagiansar, Helena; Ryll, Thomas

    2014-01-01

    In characterizing a cell culture process to support regulatory activities such as process validation and Quality by Design, developing a representative scale down model for design space definition is of great importance. The manufacturing bioreactor should ideally reproduce bench scale performance with respect to all measurable parameters. However, due to intrinsic geometric differences between scales, process performance at manufacturing scale often varies from bench scale performance, typically exhibiting differences in parameters such as cell growth, protein productivity, and/or dissolved carbon dioxide concentration. Here, we describe a case study in which a bench scale cell culture process model is developed to mimic historical manufacturing scale performance for a late stage CHO-based monoclonal antibody program. Using multivariate analysis (MVA) as primary data analysis tool in addition to traditional univariate analysis techniques to identify gaps between scales, process adjustments were implemented at bench scale resulting in an improved scale down cell culture process model. Finally we propose an approach for small scale model qualification including three main aspects: MVA, comparison of key physiological rates, and comparison of product quality attributes.

  5. Leachate/domestic wastewater aerobic co-treatment: A pilot-scale study using multivariate analysis.

    Science.gov (United States)

    Ferraz, F M; Bruni, A T; Povinelli, J; Vieira, E M

    2016-01-15

    Multivariate analysis was used to identify the variables affecting the performance of pilot-scale activated sludge (AS) reactors treating old leachate from a landfill and from domestic wastewater. Raw leachate was pre-treated using air stripping to partially remove the total ammoniacal nitrogen (TAN). The control AS reactor (AS-0%) was loaded only with domestic wastewater, whereas the other reactor was loaded with mixtures containing leachate at volumetric ratios of 2 and 5%. The best removal efficiencies were obtained for a ratio of 2%, as follows: 70 ± 4% for total suspended solids (TSS), 70 ± 3% for soluble chemical oxygen demand (SCOD), 70 ± 4% for dissolved organic carbon (DOC), and 51 ± 9% for the leachate slowly biodegradable organic matter (SBOM). Fourier transform infrared (FTIR) spectroscopic analysis confirmed that most of the SBOM was removed by partial biodegradation rather than dilution or adsorption of organics in the sludge. Nitrification was approximately 80% in the AS-0% and AS-2% reactors. No significant accumulation of heavy metals was observed for any of the tested volumetric ratios. Principal component analysis (PCA) and partial least squares (PLS) indicated that the data dimension could be reduced and that TAN, SCOD, DOC and nitrification efficiency were the main variables that affected the performance of the AS reactors. Copyright © 2015 Elsevier Ltd. All rights reserved.

  6. Multivariate statistical data analysis methods for detecting baroclinic wave interactions in the thermally driven rotating annulus

    Science.gov (United States)

    von Larcher, Thomas; Harlander, Uwe; Alexandrov, Kiril; Wang, Yongtai

    2010-05-01

    Experiments on baroclinic wave instabilities in a rotating cylindrical gap have been long performed, e.g., to unhide regular waves of different zonal wave number, to better understand the transition to the quasi-chaotic regime, and to reveal the underlying dynamical processes of complex wave flows. We present the application of appropriate multivariate data analysis methods on time series data sets acquired by the use of non-intrusive measurement techniques of a quite different nature. While the high accurate Laser-Doppler-Velocimetry (LDV ) is used for measurements of the radial velocity component at equidistant azimuthal positions, a high sensitive thermographic camera measures the surface temperature field. The measurements are performed at particular parameter points, where our former studies show that kinds of complex wave patterns occur [1, 2]. Obviously, the temperature data set has much more information content as the velocity data set due to the particular measurement techniques. Both sets of time series data are analyzed by using multivariate statistical techniques. While the LDV data sets are studied by applying the Multi-Channel Singular Spectrum Analysis (M - SSA), the temperature data sets are analyzed by applying the Empirical Orthogonal Functions (EOF ). Our goal is (a) to verify the results yielded with the analysis of the velocity data and (b) to compare the data analysis methods. Therefor, the temperature data are processed in a way to become comparable to the LDV data, i.e. reducing the size of the data set in such a manner that the temperature measurements would imaginary be performed at equidistant azimuthal positions only. This approach initially results in a great loss of information. But applying the M - SSA to the reduced temperature data sets enable us to compare the methods. [1] Th. von Larcher and C. Egbers, Experiments on transitions of baroclinic waves in a differentially heated rotating annulus, Nonlinear Processes in Geophysics

  7. Novel insights into the inhibitory mechanism of kaempferol on xanthine oxidase.

    Science.gov (United States)

    Wang, Yajie; Zhang, Guowen; Pan, Junhui; Gong, Deming

    2015-01-21

    Xanthine oxidase (XO), a key enzyme in purine catabolism, is widely distributed in human tissues. It can catalyze xanthine to generate uric acid and cause hyperuricemia and gout. Inhibition kinetics assay showed that kaempferol inhibited XO activity reversibly in a competitive manner. Strong fluorescence quenching and conformational changes of XO were found due to the formation of a kaempferol-XO complex, which was driven mainly by hydrophobic forces. The molecular docking further revealed that kaempferol inserted into the hydrophobic cavity of XO to interact with some amino acid residues. The main inhibition mechanism of kaempferol on XO activity may be due to the insertion of kaempferol into the active site of XO occupying the catalytic center of the enzyme to avoid the entrance of the substrate and inducing conformational changes of XO. In addition, luteolin exhibited a stronger synergistic effect with kaempferol than did morin at the lower concentration.

  8. Analysis of multi-species point patterns using multivariate log Gaussian Cox processes

    DEFF Research Database (Denmark)

    Waagepetersen, Rasmus; Guan, Yongtao; Jalilian, Abdollah;

    Multivariate log Gaussian Cox processes are flexible models for multivariate point patterns. However, they have so far only been applied in bivariate cases. In this paper we move beyond the bivariate case in order to model multi-species point patterns of tree locations. In particular we address...

  9. An analysis of longitudinal data with nonignorable dropout using the truncated multivariate normal distribution

    NARCIS (Netherlands)

    Jolani, Shahab

    2014-01-01

    For a vector of multivariate normal when some elements, but not necessarily all, are truncated, we derive the moment generating function and obtain expressions for the first two moments involving the multivariate hazard gradient. To show one of many applications of these moments, we then extend the

  10. Multivariate meta-analysis of individual participant data helped externally validate the performance and implementation of a prediction model

    NARCIS (Netherlands)

    K.I.E. Snell (Kym I.E.); H. Hua (Harry); T.P. Debray (Thomas P.A.); J. Ensor (Joie); M.P. Look (Maxime); K.G.M. Moons (Karel G.M.); R.D. Riley (Richard D.)

    2016-01-01

    textabstractObjectives Our aim was to improve meta-analysis methods for summarizing a prediction model's performance when individual participant data are available from multiple studies for external validation. Study Design and Setting We suggest multivariate meta-analysis for jointly synthesizing c

  11. Multivariate areal analysis of the impact and efficiency of the family planning programme in peninsular Malaysia.

    Science.gov (United States)

    Tan Boon Ann

    1987-06-01

    The findings of the final phase of a 3-phase multivariate areal analysis study undertaken by the Economic and Social Commission for Asia and the Pacific (ESCAP) in 5 countries of the Asian and Pacific Region, including Malaysia, to examine the impact of family planning programs on fertility and reproduction are reported. The study used Malaysia's administrative district as the unit of analysis because the administration and implementation of socioeconomic development activities, as well as the family planning program, depend to a large extent on the decisions of local organizations at the district or state level. In phase 1, existing program and nonprogram data were analyzed using the multivariate technique to separate the impact of the family planning program net of other developmental efforts. The methodology in the 2nd phase consisted of in-depth investigation of selected areas in order to discern the dynamics and determinants of efficiency. The insights gained in phase 2 regarding dynamics of performance were used in phase 3 to refine the input variables of the phase 1 model. Thereafter, the phase 1 analysis was repeated. Insignificant variables and factors were trimmed in order to present a simplified model for studying the impact of environmental, socioeconomic development, family planning programs, and related factors on fertility. The inclusion of a set of family planning program and development variables in phase 3 increased the predictive power of the impact model. THe explained variance for total fertility rate (TFR) of women under 30 years increased from 71% in phase 1 to 79%. It also raised the explained variance of the efficiency model from 34% to 70%. For women age 30 years and older, their TFR was affected directly by the ethnic composition variable (.76), secondary educational status (-.45), and modern nonagricultural occupation (.42), among others. When controlled for other socioeconomic development and environmental indicators, the

  12. Multivariate analysis in relation to breeding system in opium popy, Papaver somniferum L.

    Directory of Open Access Journals (Sweden)

    Singh S.P.

    2004-01-01

    Full Text Available The opium poppy (Papaver somniferum L. is an important medicinal plant of great pharmacopoel uses. 101 germplasm lines of different eco-geographical origin maintained at National Botanical Research Institute, Lucknow were evaluated to study the genetic divergence for seed yield/plant, opium yield/plant and its 8 component traits following multivariate and canonical analysis. The genotypes were grouped in 13 clusters and confirmed by canonical analysis. Sixty eight percent genotypes (69/101 were genetically close to each other and grouped in 6 clusters (II, III, IV, V, VIII, XII while apparent diversity was noticed for 32 percent (32/101 of the genotypes who diversed into rest 7 clusters (I, VI, VII, IX, X, XI, XIII. Inter cluster distance ranged from 47.28 to 234.55. The maximum was between IX and X followed by VII and IX (208.30 and IX and XI (205.53. The genotypes in cluster IX, X. XI, and XII had greater potential as breeding stock by virtue of high mean values of one or more component characters and high statistical distance among them. Based on findings of high cluster mean of component trait and inter-cluster distance among clusters, a breeding plan has been discussed.

  13. Tools based on multivariate statistical analysis for classification of soil and groundwater in Apulian agricultural sites.

    Science.gov (United States)

    Ielpo, Pierina; Leardi, Riccardo; Pappagallo, Giuseppe; Uricchio, Vito Felice

    2017-06-01

    In this paper, the results obtained from multivariate statistical techniques such as PCA (Principal component analysis) and LDA (Linear discriminant analysis) applied to a wide soil data set are presented. The results have been compared with those obtained on a groundwater data set, whose samples were collected together with soil ones, within the project "Improvement of the Regional Agro-meteorological Monitoring Network (2004-2007)". LDA, applied to soil data, has allowed to distinguish the geographical origin of the sample from either one of the two macroaeras: Bari and Foggia provinces vs Brindisi, Lecce e Taranto provinces, with a percentage of correct prediction in cross validation of 87%. In the case of the groundwater data set, the best classification was obtained when the samples were grouped into three macroareas: Foggia province, Bari province and Brindisi, Lecce and Taranto provinces, by reaching a percentage of correct predictions in cross validation of 84%. The obtained information can be very useful in supporting soil and water resource management, such as the reduction of water consumption and the reduction of energy and chemical (nutrients and pesticides) inputs in agriculture.

  14. Multivariate analysis of variance of designed chromatographic data. A case study involving fermentation of rooibos tea.

    Science.gov (United States)

    Marini, Federico; de Beer, Dalene; Walters, Nico A; de Villiers, André; Joubert, Elizabeth; Walczak, Beata

    2017-03-17

    An ultimate goal of investigations of rooibos plant material subjected to different stages of fermentation is to identify the chemical changes taking place in the phenolic composition, using an untargeted approach and chromatographic fingerprints. Realization of this goal requires, among others, identification of the main components of the plant material involved in chemical reactions during the fermentation process. Quantitative chromatographic data for the compounds for extracts of green, semi-fermented and fermented rooibos form the basis of preliminary study following a targeted approach. The aim is to estimate whether treatment has a significant effect based on all quantified compounds and to identify the compounds, which contribute significantly to it. Analysis of variance is performed using modern multivariate methods such as ANOVA-Simultaneous Component Analysis, ANOVA - Target Projection and regularized MANOVA. This study is the first one in which all three approaches are compared and evaluated. For the data studied, all tree methods reveal the same significance of the fermentation effect on the extract compositions, but they lead to its different interpretation.

  15. Risk factors for non-alcoholic fatty liver disease: a multivariate analysis

    Directory of Open Access Journals (Sweden)

    PANG Xueqin

    2014-09-01

    Full Text Available ObjectiveTo investigate the risk factors for non-alcoholic fatty liver disease (NAFLD and to provide a basis for the prevention of NAFLD. MethodsA total of 190 patients with NAFLD who visited the First Affiliated Hospital of Soochow University from January 2011 to January 2013 were included in the study. The investigated factors included sex, age, height, weight, dietary habit, smoking and alcohol consumption, educational level, occupation, intensity and duration of physical exercise, bedtime, previous history, and family history. Univariate and multivariate analyses were performed using SPSS 18.0 to determine the risk factors for NAFLD. ResultsThe univariate analysis showed that sex, age, dietary habit, occupation, body mass index (BMI, and educational level were associated with NAFLD (P<0.05. The logistic regression analysis showed that the risk factors for NAFLD were sex (OR=5.692, P=0.029, age (OR=0.423, P=0.041, occupation (OR=0.698, P=0.008, BMI (OR=3.939, P=0.003, educational level (OR=5.463, P=0.030, and dietary habit (OR=9.235, P=0.039. ConclusionNAFLD may be related to many factors, and corresponding preventive measures may reduce the development of NAFLD.

  16. Detection of neuroinflammation through the retina by means of Raman spectroscopy and multivariate analysis

    Science.gov (United States)

    Marro, Monica; Taubes, Alice; Villoslada, Pablo; Petrov, Dmitri

    2012-06-01

    Retinal nervous tissue sustains a substantial damage during the autoimmune inflammatory processes characteristic for Multiple Sclerosis (MS). The damage can be characterized non-surgically by Raman Spectroscopy, a non-invasive optical imaging technology. We used non-resonant near-infrared Raman spectrosocopy to create a spectral library of eight pivotal biomolecules known to be involved in neuroinflammation: Nicotinamide Adenine Dinucliotide (NADH), Flavin Adenine Nucleotide (FAD), Lactate, Cytochrome C, Glutamate, N-Acetyl- Aspartate (NAA), Phosphotidylcholine, with Advanced Glycolization End Products (AGEs) analyzed as a reference. Principal Component Analysis (PCA) of 50 spectra taken of murine retinal tissue culture undergoing an inflammatory response and healthy controls was used in order to characterize the molecular makeup of the inflammation. The loading plots revealed a heavy influence of peaks related to Glutamate, NADH, and Phosphotidylcholine to inflammation-related spectral changes. Partial Least Squares - Discriminant analysis (PLS-DA) was performed to create a multivariate classifier for the spectral diagnosis of neuroinflammed tissue and yielded a diagnostic sensitivity of 100% and specificity of 100%. We demonstrate then the effectiveness of combining Raman spectroscopy with PCA and PLS-DA statistical techniques to detect and monitor neuroinflamation in retina. With this technique Glutamate, NAA and NADH are detected in retina tissue as signs for neuroinflammation.

  17. Identification of Chemical Attribution Signatures of Fentanyl Syntheses Using Multivariate Statistical Analysis of Orthogonal Analytical Data

    Energy Technology Data Exchange (ETDEWEB)

    Mayer, B. P. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Mew, D. A. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); DeHope, A. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Spackman, P. E. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Williams, A. M. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2015-09-24

    Attribution of the origin of an illicit drug relies on identification of compounds indicative of its clandestine production and is a key component of many modern forensic investigations. The results of these studies can yield detailed information on method of manufacture, starting material source, and final product - all critical forensic evidence. In the present work, chemical attribution signatures (CAS) associated with the synthesis of the analgesic fentanyl, N-(1-phenylethylpiperidin-4-yl)-N-phenylpropanamide, were investigated. Six synthesis methods, all previously published fentanyl synthetic routes or hybrid versions thereof, were studied in an effort to identify and classify route-specific signatures. 160 distinct compounds and inorganic species were identified using gas and liquid chromatographies combined with mass spectrometric methods (GC-MS and LCMS/ MS-TOF) in conjunction with inductively coupled plasma mass spectrometry (ICPMS). The complexity of the resultant data matrix urged the use of multivariate statistical analysis. Using partial least squares discriminant analysis (PLS-DA), 87 route-specific CAS were classified and a statistical model capable of predicting the method of fentanyl synthesis was validated and tested against CAS profiles from crude fentanyl products deposited and later extracted from two operationally relevant surfaces: stainless steel and vinyl tile. This work provides the most detailed fentanyl CAS investigation to date by using orthogonal mass spectral data to identify CAS of forensic significance for illicit drug detection, profiling, and attribution.

  18. Multivariate statistical analysis of meteorological and air pollution data in the 'Campo de Gibraltar' region, Spain.

    Science.gov (United States)

    González Gallero, Francisco Javier; Galán Vallejo, Manuel; Umbría, Arturo; Gervilla Baena, Juan

    2006-08-01

    A complete statistical analysis of meteorological and air pollution data was carried out in the 'Campo de Gibraltar' region (in the South of Spain) from 1999 to 2002. This is a heavy industrialized area where, up to date, very few air pollution studies have been made. The main objectives of the study presented here have been the characterization of the meteorological and (gaseous and particulate) air pollution conditions in the area, and the relations between them. Multivariate statistical techniques, such as Principal Component Analysis (PCA), have been applied to the data. The results show that air quality in the area is highly dependent on meteorological conditions such as wind persistence and direction, dispersion capability of the atmosphere, and humidity content. On average, sulphur dioxide and nitrogen oxide air pollution, mainly caused by fuel-oil combustion and traffic, respectively, is not very high. However, an important number of exceedences of the limits established by the EU Directive 1999 for PM10 (particulate matter with a diameter less than 10 microm) have been observed in some points of the area. A significant percentage of these exceedences (about 22% on average) are likely caused by African dust intrusions, which usually take place from May to August. From gaseous and particulate air correlations, it seems that anthropogenic activities contribute with about 19% on average.

  19. Are Risk Attitudes and Individualism Predictors of Entrepreneurship? A Multivariate Analysis of Romanian Data

    Directory of Open Access Journals (Sweden)

    Adrian Hatos

    2015-02-01

    Full Text Available This paper emerges in the context of authors` previous investigations concerning the individual determinants of entrepreneurship. More specific, it focuses on elaborating and empirically testing hypotheses related to structural push and pull factors, e.g. age, gender, education, type of residence, and also to two kinds of psycho-attitudinal factors, i.e. risk aversion and individualist vs. etatist economic ideology. While the literature review gives credit to both hypotheses, especially for the influence of risk attitudes on starting a business, this paper focuses on the analysis of self-employment by using the block-model logistic regression on 2008 Romanian EVS (European Values Survey data. The results of multivariate analysis confirm the importance of risk aversion for entrepreneurship, as expected, but reject the hypothesis of a significant effect of individual’s option for individualist vs. collectivist (or statist continuum. It is important to notice that, contrary to expectations, two important push factors, i.e. age and education, do not correlate with self-employment and, on the other hand, risk attitude adds itself to the other effects without interacting with it. The theoretical consequences of the findings, the limits of the research and further developments are also discussed in the paper.

  20. Fingerprinting of morphine using chromatographic purity profiling and multivariate data analysis.

    Science.gov (United States)

    Acevska, Jelena; Stefkov, Gjoshe; Cvetkovikj, Ivana; Petkovska, Rumenka; Kulevanova, Svetlana; Cho, JungHwan; Dimitrovska, Aneta

    2015-05-10

    Chromatographic purity profiling (CPP) is the common name of a group of analytical and chemometric applications for detection, identification and quantitative determination of related substances and other impurities in active pharmaceutical ingredients (APIs) and finished dosage forms (FDFs). CPP is used for fingerprinting and discriminating between samples, thus representing a core activity in modern drug analysis. The worldwide demand for morphine and its congeners is tremendous and depends entirely on the supply of natural opiates. The aim of this research was to develop a methodology that enables identification of a source of morphine, thus revealing falsification of the substance. The characteristic and reproducible features of impurity profiles for 28 samples of morphine (6 morphine sulfate, 9 morphine hydrochloride and 13 morphine base) were captured by a new LC/MS method for impurity profiling of morphine. The impurity profile encompasses the related substances specified in relevant Ph.Eur. monographs, as well as the other morphinane like impurities, including the naturally occurring co-extracted alkaloids. Different pattern recognition techniques (unsupervised and supervised) were used to reveal the differentiation features of the morphine fingerprints for classification and authentication purposes. The results described in this research open the possibility of using the chromatographic purity profile combined with multivariate data analysis for fingerprinting of morphine samples.

  1. Assessment of the effect of silicon on antioxidant enzymes in cotton plants by multivariate analysis.

    Science.gov (United States)

    Alberto Moldes, Carlos; Fontão de Lima Filho, Oscar; Manuel Camiña, José; Gabriela Kiriachek, Soraya; Lia Molas, María; Mui Tsai, Siu

    2013-11-27

    Silicon has been extensively researched in relation to the response of plants to biotic and abiotic stress, as an element triggering defense mechanisms which activate the antioxidant system. Furthermore, in some species, adding silicon to unstressed plants modifies the activity of certain antioxidant enzymes participating in detoxifying processes. Thus, in this study, we analyzed the activity of antioxidant enzymes in leaves and roots of unstressed cotton plants fertilized with silicon (Si). Cotton plants were grown in hydroponic culture and added with increasing doses of potassium silicate; then, the enzymatic activity of catalase (CAT), guaiacol peroxidase (GPOX), ascorbate peroxidase (APX), and lipid peroxidation were determined. Using multivariate analysis, we found that silicon altered the activity of GPOX, APX, and CAT in roots and leaves of unstressed cotton plants, whereas lipid peroxidation was not affected. The analysis of these four variables in concert showed a clear differentiation among Si treatments. We observed that enzymatic activities in leaves and roots changed as silicon concentration increased, to stabilize at 100 and 200 mg Si L(-1) treatments in leaves and roots, respectively. Those alterations would allow a new biochemical status that could be partially responsible for the beneficial effects of silicon. This study might contribute to adjust the silicon application doses for optimal fertilization, preventing potential toxic effects and unnecessary cost.

  2. Time-varying nonstationary multivariate risk analysis using a dynamic Bayesian copula

    Science.gov (United States)

    Sarhadi, Ali; Burn, Donald H.; Concepción Ausín, María.; Wiper, Michael P.

    2016-03-01

    A time-varying risk analysis is proposed for an adaptive design framework in nonstationary conditions arising from climate change. A Bayesian, dynamic conditional copula is developed for modeling the time-varying dependence structure between mixed continuous and discrete multiattributes of multidimensional hydrometeorological phenomena. Joint Bayesian inference is carried out to fit the marginals and copula in an illustrative example using an adaptive, Gibbs Markov Chain Monte Carlo (MCMC) sampler. Posterior mean estimates and credible intervals are provided for the model parameters and the Deviance Information Criterion (DIC) is used to select the model that best captures different forms of nonstationarity over time. This study also introduces a fully Bayesian, time-varying joint return period for multivariate time-dependent risk analysis in nonstationary environments. The results demonstrate that the nature and the risk of extreme-climate multidimensional processes are changed over time under the impact of climate change, and accordingly the long-term decision making strategies should be updated based on the anomalies of the nonstationary environment.

  3. Student construction of differential length elements in multivariable coordinate systems: A symbolic forms analysis

    Science.gov (United States)

    Thompson, John; Schermerhorn, Benjamin

    2017-01-01

    Analysis of properties of physical quantities represented by vector fields often involves symmetries and spatial relationships best expressed in non-Cartesian coordinate systems. Many important quantities are determined by integrals that can involve multivariable vector differential quantities. Four pairs of students in junior-level Electricity and Magnetism (E&M) were interviewed to investigate their understanding of the structure of non-Cartesian coordinate systems and the associated differential elements. Pairs were asked to construct differential length elements for an unconventional spherical coordinate system. In order to explore how student conceptual understanding interacts with their understanding of the specific structures of these expressions, a symbolic forms framework was used. Analysis of student reasoning revealed both known and novel forms as well as the general progression of students--use and combination of symbol templates during the construction process. Each group invoked and combined symbolic forms in a similar sequence. Difficulties with the construction of expressions seem to be related almost exclusively to the conceptual schema (e.g., neglecting the role of projection) rather than with symbol templates. Supported in part by NSF Grant PHY-1405726.

  4. Dispersive Raman spectroscopy and multivariate data analysis to detect offal adulteration of thawed beefburgers.

    Science.gov (United States)

    Zhao, Ming; Downey, Gerard; O'Donnell, Colm P

    2015-02-11

    Beef offal (i.e., kidney, liver, heart, lung) adulteration of beefburgers was studied using dispersive Raman spectroscopy and multivariate data analysis to explore the potential of these analytical tools for detection of adulterations in comminuted meat products with complex formulations. Adulterated (n = 46) and authentic (n = 36) beefburger samples were produced based on formulations derived using market knowledge and an experimental design. Raman spectral data in the fingerprint range (900-1800 cm(-1)) were examined using both a classification (partial least-squares discriminant analysis, PLS-DA) and class-modeling (soft independent modeling of class analogy, SIMCA) approach to identify offal-adulterated and authentic beefburgers. PLS-DA models correctly classified 89-100% of authentic and 90-100% of adulterated samples. SIMCA models were developed using either PCA or PLS scores as input data. For authentic beefburgers, they exhibited sensitivity, specificity, and efficiency values of 0.94-1, 0.64-1, and 0.80-0.97, respectively. PLS regression quantitative models were also developed in an attempt to quantify total offal and added fat in these samples. The performance of PLS regression quantitative models for prediction of added fat may be acceptable for screening purposes, with the most accurate model producing a coefficient of determination in prediction of 0.85 and a root-mean-square error of prediction equal to 3.8% w/w.

  5. Multivariate analysis for quantification of Plutonium (IV) in nitric acid based on absorption spectra

    Energy Technology Data Exchange (ETDEWEB)

    Lines, Amanda M.; Adami, Susan R.; Sinkov, Sergey I.; Lumetta, Gregg J.; Bryan, Samuel A.

    2017-07-20

    Development of more effective, reliable, and fast methods for monitoring process streams is a growing opportunity for analytical applications. Many fields can benefit from on-line monitoring, including the nuclear fuel cycle where improved methods for monitoring radioactive materials will facilitate maintenance of proper safeguards and ensure safe and efficient processing of materials. On-line process monitoring with a focus on optical spectroscopy can provide a fast, non-destructive method for monitoring chemical species. However, identification and quantification of species can be hindered by the complexity of the solutions if bands overlap or show condition-dependent spectral features. Plutonium (IV) is one example of a species which displays significant spectral variation with changing nitric acid concentration. Single variate analysis (i.e. Beer’s Law) is difficult to apply to the quantification of Pu(IV) unless the nitric acid concentration is known and separate calibration curves have been made for all possible acid strengths. Multivariate, or chemometric, analysis is an approach that allows for the accurate quantification of Pu(IV) without a priori knowledge of nitric acid concentration.

  6. [A Multivariate Analysis of the Efficacy of Adjuvant Chemotherapy in Triple-Negative Breast Cancer].

    Science.gov (United States)

    Nio, Yoshinori; Imai, Shiro; Uesugi, Kayo; Tamaoki, Mikako; Tamaoki, Masashi; Maruyama, Riruke

    2016-10-01

    Triple-negative breast cancers(TNBCs)are associated with early recurrence after surgery and unfavorable prognoses. To date, no effective therapies for TNBCs have been established. The present study was designed to evaluate the efficacy of adjuvant chemotherapy(ACT)for 111 TNBCs using a retrospective multivariate analysis(MVA). The intravenous(iv)ACTs included docetaxel, epirubicin, gemcitabine, and vinorelbine. The oral ACTs included UFT, doxifluridine, and cyclophosphamide. The 10-year disease-free survival(DFS)and overall survival(OS)rates were 77.5% and 86.0%, respectively. Recurrences were observed in 17 patients, and the first recurrence was most frequently located in the lung. MVA revealed that pT was a significant independent variable for poor DFS and OS. UFT was the only significant independent variable for improved DFS. The survival analysis also demonstrated that UFT alone may be an effective option for Stage I TNBCs. Furthermore, it suggested that the addition of further iv ACTs to UFT could improve the outcome in patients with Stage II-III TNBCs.

  7. Elemental Mapping of Perovskite Solar Cells by Using Multivariate Analysis: An Insight into Degradation Processes.

    Science.gov (United States)

    Cacovich, Stefania; Divitini, Giorgio; Ireland, Christopher; Matteocci, Fabio; Di Carlo, Aldo; Ducati, Caterina

    2016-09-22

    The technology of perovskite-based solar cells is evolving rapidly, reaching certified power conversion efficiency values now exceeding 20 %. One of the main drawbacks hindering progress in the field is the long-term stability of the cells: the mixed halide perovskites used in most devices are sensitive to humidity and degrade on a timescale varying from hours to weeks. The degradation mechanisms are poorly understood, but likely arise from combined physical and chemical modifications at the nanometer scale. The characterization of pristine and degraded materials is difficult owing to their complex chemical and physical structure and their relatively poor stability. In this work, we investigated the changes in local composition and morphology of a standard device after 2 months of air exposure in the dark, using scanning transmission electron microscopy (STEM) with nanometer resolution for imaging and analysis. Because of a state-of-the-art technique that combines STEM and energy dispersive X-ray spectroscopy (EDX), and the use of different decomposition algorithms for multivariate analysis, we highlighted the migration of elements across the interfaces between the layers comprising the device. We also noticed a morphological degradation of the hole-transporting layer (HTL), representing one of the main factors enabling the infiltration of moisture in the device, which results in reduced performance. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  8. Multivariate analysis of buckwheat sourdough fermentations for metabolic screening of starter cultures.

    Science.gov (United States)

    Capuani, Alessandro; Stetina, Mandy; Gstattenbauer, Anja; Behr, Jürgen; Vogel, Rudi F

    2014-08-18

    This study investigated the metabolic activity of 35 strains of lactic acid bacteria (LAB), which were able to grow in buckwheat sourdoughs and delivers a detailed explanation of LAB metabolism in that environment. To interpret the high-dimensional dataset, descriptive statistics and linear discriminant analysis (LDA) were used. Heterofermentative LAB showed a clear different metabolism than facultative (f.) heterofermentative and homofermentative LAB, which were more similar. Heterofermentative LAB were mainly characterized by high free SH groups and acetic acid production; they were also able to consume arabinose and glucose. Homofermenters were mainly characterized by lower free amino nitrogen content and they did not show a good capacity to consume arabinose and fructose. Except for the heterofermentative Weissella cibaria strain, only homofermentative strains showed high ornithine yields. Some f. heterofermentative strains differed from homofermentative due to the high lactic acid production as well as low glucose and arginine consumption. LAB containing more genes encoding peptidase activities and genes involved in aroma production showed a high consumption of free amino acids. Strain-dependent activities could be clearly distinguished from group dependent ones (homofermentative, f. heterofermentative and heterofermentative), e.g., some Lactobacillus paracasei and Lactobacillus plantarum strains showed the highest carbohydrate consumption. However, some microbial activities were more strain-dependent than group-dependent. Multivariate analysis of raw data delivered a detailed and clear explanation of LAB metabolism in buckwheat sourdough fermentations. Copyright © 2014 Elsevier B.V. All rights reserved.

  9. Assessing heavy metal sources in sugarcane Brazilian soils: an approach using multivariate analysis.

    Science.gov (United States)

    da Silva, Fernando Bruno Vieira; do Nascimento, Clístenes Williams Araújo; Araújo, Paula Renata Muniz; da Silva, Luiz Henrique Vieira; da Silva, Roberto Felipe

    2016-08-01

    Brazil is the world's largest sugarcane producer and soils in the northeastern part of the country have been cultivated with the crop for over 450 years. However, so far, there has been no study on the status of heavy metal accumulation in these long-history cultivated soils. To fill the gap, we collect soil samples from 60 sugarcane fields in order to determine the contents of Cd, Cr, Cu, Ni, Pb, and Zn. We used multivariate analysis to distinguish between natural and anthropogenic sources of these metals in soils. Analytical determinations were performed in ICP-OES after microwave acid solution digestion. Mean concentrations of Cd, Cr, Cu, Ni, Pb, and Zn were 1.9, 18.8, 6.4, 4.9, 11.2, and 16.2 mg kg(-1), respectively. The principal component one was associated with lithogenic origin and comprised the metals Cr, Cu, Ni, and Zn. Cluster analysis confirmed that 68 % of the evaluated sites have soil heavy metal concentrations close to the natural background. The Cd concentration (principal component two) was clearly associated with anthropogenic sources with P fertilization being the most likely source of Cd to soils. On the other hand, the third component (Pb concentration) indicates a mixed origin for this metal (natural and anthropogenic); hence, Pb concentrations are probably related not only to the soil parent material but also to industrial emissions and urbanization in the vicinity of the agricultural areas.

  10. Tunable-Q Wavelet Transform Based Multivariate Sub-Band Fuzzy Entropy with Application to Focal EEG Signal Analysis

    Directory of Open Access Journals (Sweden)

    Abhijit Bhattacharyya

    2017-03-01

    Full Text Available This paper analyses the complexity of multivariate electroencephalogram (EEG signals in different frequency scales for the analysis and classification of focal and non-focal EEG signals. The proposed multivariate sub-band entropy measure has been built based on tunable-Q wavelet transform (TQWT. In the field of multivariate entropy analysis, recent studies have performed analysis of biomedical signals with a multi-level filtering approach. This approach has become a useful tool for measuring inherent complexity of the biomedical signals. However, these methods may not be well suited for quantifying the complexity of the individual multivariate sub-bands of the analysed signal. In this present study, we have tried to resolve this difficulty by employing TQWT for analysing the sub-band signals of the analysed multivariate signal. It should be noted that higher value of Q factor is suitable for analysing signals with oscillatory nature, whereas the lower value of Q factor is suitable for analysing signals with non-oscillatory transients in nature. Moreover, with an increased number of sub-bands and a higher value of Q-factor, a reasonably good resolution can be achieved simultaneously in high and low frequency regions of the considered signals. Finally, we have employed multivariate fuzzy entropy (mvFE to the multivariate sub-band signals obtained from the analysed signal. The proposed Q-based multivariate sub-band entropy has been studied on the publicly available bivariate Bern Barcelona focal and non-focal EEG signals database to investigate the statistical significance of the proposed features in different time segmented signals. Finally, the features are fed to random forest and least squares support vector machine (LS-SVM classifiers to select the best classifier. Our method has achieved the highest classification accuracy of 84.67% in classifying focal and non-focal EEG signals with LS-SVM classifier. The proposed multivariate sub-band fuzzy

  11. Mechanisms Underlying Apoptosis-Inducing Effects of Kaempferol in HT-29 Human Colon Cancer Cells

    OpenAIRE

    Hyun Sook Lee; Han Jin Cho; Rina Yu; Ki Won Lee; Hyang Sook Chun; Jung Han Yoon Park

    2014-01-01

    We previously noted that kaempferol, a flavonol present in vegetables and fruits, reduced cell cycle progression of HT-29 cells. To examine whether kaempferol induces apoptosis of HT-29 cells and to explore the underlying molecular mechanisms, cells were treated with various concentrations (0–60 μmol/L) of kaempferol and analyzed by Hoechst staining, Annexin V staining, JC-1 labeling of the mitochondria, immunoprecipitation, in vitro kinase assays, Western blot analyses, and caspase-8 assays...

  12. A simple ergonomic measure reduces fluoroscopy time during ERCP: A multivariate analysis

    Science.gov (United States)

    Jowhari, Fahd; Hopman, Wilma M.; Hookey, Lawrence

    2017-01-01

    Background and study aims Endoscopic retrograde cholangiopancreatgraphy (ERCP) carries a radiation risk to patients undergoing the procedure and the team performing it. Fluoroscopy time (FT) has been shown to have a linear relationship with radiation exposure during ERCP. Recent modifications to our ERCP suite design were felt to impact fluoroscopy time and ergonomics. This multivariate analysis was therefore undertaken to investigate these effects, and to identify and validate various clinical, procedural and ergonomic factors influencing the total fluoroscopy time during ERCP. This would better assist clinicians with predicting prolonged fluoroscopic durations and to undertake relevant precautions accordingly. Patients and methods A retrospective analysis of 299 ERCPs performed by 4 endoscopists over an 18-month period, at a single tertiary care center was conducted. All inpatients/outpatients (121 males, 178 females) undergoing ERCP for any clinical indication from January 2012 to June 2013 in the chosen ERCP suite were included in the study. Various predetermined clinical, procedural and ergonomic factors were obtained via chart review. Univariate analyses identified factors to be included in the multivariate regression model with FT as the dependent variable. Results Bringing the endoscopy and fluoroscopy screens next to each other was associated with a significantly lesser FT than when the screens were separated further (–1.4 min, P = 0.026). Other significant factors associated with a prolonged FT included having a prior ERCP (+ 1.4 min, P = 0.031), and more difficult procedures (+ 4.2 min for each level of difficulty, P high-volume endoscopists used lesser FT vs. low-volume endoscopists (–1.82, P = 0.015). Conclusions Our study has identified and validated various factors that affect the total fluoroscopy time during ERCP. This is the first study to show that decreasing the distance between the endoscopy and fluoroscopy

  13. MULTIVARIATE ANALYSIS OF RELATIONSHIPS BETWEEN IODINE BIOLOGICAL EXPOSURE AND SUBCLINICAL THYROID DYSFUNCTIONS

    Institute of Scientific and Technical Information of China (English)

    Wei Chong; Zhong-yan Shan; Wei Sun; Wei-ping Teng

    2005-01-01

    Objective To assess the relationships between iodine biological exposure and subclinical thyroid dysfunctions. Methods The cross-sectional survey was performed to obtain the epidemiologic data of population in three communities with different iodine biological exposure: mild iodine deficiency [median urinary iodine concentration (MUI) of 50 99g/L], more than adequate iodine intake (MUI of 200-299 μg/L), and excessive iodine intake (MUI over 300 μg/L). Univariate and multivariate analysis (logistic regression analysis) were used to analyze the risk factors of subclinical hypothyroidism and subclinical hyperthyroidism. Results Logistic regression analysis with sex and age controlled suggested that more than adequate iodine intake (OR = 3.172, P = 0.0004) and excessive iodine intake (OR = 6.391, P = 0.0001) increased the risk of subclinical hypothyroidism, while excessive iodine intake decreased the risk of subclinical hyperthyroidism (OR = 0.218, P= 0.0001). Logistic regression analysis including interaction of iodine intake and antibodies [tryroid peroxidase antibody (TPOAb) and thyroglobulin antibody (TgAb)] suggested that excessive iodine intake was an independent risk factor of subclinical hypothyroidism (OR = 6.360, P = 0.0001), but independent protect factor of subclinical hyperthyroidism (OR = 0.193, P = 0.0001). More than adequate iodine intake and it's interaction with TgAb increased the risk of subclinical hypothyroidism independently, in addition, it decreased the risk of subclinical hyperthyroidism at the present of TPOAb. Conclusion Both excessive iodine intake and more than adequate iodine intake could increase risk of subclinical hypo thyroidism, supplement of iodine should be controlled to ensure MUI within the safe range.

  14. Investigating the provenance of thermal groundwater using compositional multivariate statistical analysis: a hydrogeochemical study from Ireland

    Science.gov (United States)

    Blake, Sarah; Henry, Tiernan; Murray, John; Flood, Rory; Muller, Mark R.; Jones, Alan G.; Rath, Volker

    2016-04-01

    The geothermal energy of thermal groundwater is currently being exploited for district-scale heating in many locations world-wide. The chemical compositions of these thermal waters reflect the provenance and hydrothermal circulation patterns of the groundwater, which are controlled by recharge, rock type and geological structure. Exploring the provenance of these waters using multivariate statistical analysis (MSA) techniques increases our understanding of the hydrothermal circulation systems, and provides a reliable tool for assessing these resources. Hydrochemical data from thermal springs situated in the Carboniferous Dublin Basin in east-central Ireland were explored using MSA, including hierarchical cluster analysis (HCA) and principal component analysis (PCA), to investigate the source aquifers of the thermal groundwaters. To take into account the compositional nature of the hydrochemical data, compositional data analysis (CoDa) techniques were used to process the data prior to the MSA. The results of the MSA were examined alongside detailed time-lapse temperature measurements from several of the springs, and indicate the influence of three important hydrogeological processes on the hydrochemistry of the thermal waters: 1) increased salinity due to evaporite dissolution and increased water-rock-interaction; 2) dissolution of carbonates; and 3) dissolution of metal sulfides and oxides associated with mineral deposits. The use of MSA within the CoDa framework identified subtle temporal variations in the hydrochemistry of the thermal springs, which could not be identified with more traditional graphing methods (e.g., Piper diagrams), or with a standard statistical approach. The MSA was successful in distinguishing different geological settings and different annual behaviours within the group of springs. This study demonstrates the usefulness of the application of MSA within the CoDa framework in order to better understand the underlying controlling processes

  15. Kaempferol induces chondrogenesis in ATDC5 cells through activation of ERK/BMP-2 signaling pathway.

    Science.gov (United States)

    Nepal, Manoj; Li, Liang; Cho, Hyoung Kwon; Park, Jong Kun; Soh, Yunjo

    2013-12-01

    Endochondral bone formation occurs when mesenchymal cells condense to differentiate into chondrocytes, the primary cell types of cartilage. The aim of the present study was to identify novel factors regulating chondrogenesis. We investigated whether kaempferol induces chondrogenic differentiation in clonal mouse chondrogenic ATDC5 cells. Kaempferol treatment stimulated the accumulation of cartilage nodules in a dose-dependent manner. Kaempferol-treated ATDC5 cells stained more intensely with alcian blue staining than control cells, suggesting greater synthesis of matrix proteoglycans in the kaempferol-treated cells. Similarly, kaempferol induced greater activation of alkaline phosphatase activity than control cells, and it enhanced the expression of chondrogenic marker genes, such as collagen type I, collagen type X, OCN, Runx2, and Sox9. Kaempferol induced an acute activation of extracellular signal-regulated kinase (ERK) but not c-jun N-terminal kinase or p38 MAP kinase. PD98059, an inhibitor of MAPK/ERK, decreased in stained cells treated with kaempferol. Furthermore, kaempferol greatly expressed the protein and mRNA levels of BMP-2, suggesting chondrogenesis was stimulated via a BMP-2 pathway. Taken together, our results suggest that kaempferol has chondromodulating effects via an ERK/BMP-2 signaling pathway and could potentially be used as a therapeutic agent for bone growth disorders.

  16. A review of the dietary flavonoid, kaempferol on human health and cancer chemoprevention.

    Science.gov (United States)

    Chen, Allen Y; Chen, Yi Charlie

    2013-06-15

    Kaempferol is a polyphenol antioxidant found in fruits and vegetables. Many studies have described the beneficial effects of dietary kaempferol in reducing the risk of chronic diseases, especially cancer. Epidemiological studies have shown an inverse relationship between kaempferol intake and cancer. Kaempferol may help by augmenting the body's antioxidant defence against free radicals, which promote the development of cancer. At the molecular level, kaempferol has been reported to modulate a number of key elements in cellular signal transduction pathways linked to apoptosis, angiogenesis, inflammation, and metastasis. Significantly, kaempferol inhibits cancer cell growth and angiogenesis and induces cancer cell apoptosis, but on the other hand, kaempferol appears to preserve normal cell viability, in some cases exerting a protective effect. The aim of this review is to synthesize information concerning the extraction of kaempferol, as well as to provide insights into the molecular basis of its potential chemo-preventative activities, with an emphasis on its ability to control intracellular signaling cascades that regulate the aforementioned processes. Chemoprevention using nanotechnology to improve the bioavailability of kaempferol is also discussed.

  17. A Computer-Based Content Analysis of Interview Texts: Numeric Description and Multivariate Analysis.

    Science.gov (United States)

    Bierschenk, B.

    1977-01-01

    A method is described by which cognitive structures in verbal data can be identified and categorized through numerical analysis and quantitative description. Transcriptions of interviews (in this case, the verbal statements of 40 researchers) are manually coded and subjected to analysis following the AaO (Agent action Object) paradigm. The texts…

  18. Multivariate Statistical Analysis Software Technologies for Astrophysical Research Involving Large Data Bases

    Science.gov (United States)

    Djorgovski, S. G.

    1994-01-01

    We developed a package to process and analyze the data from the digital version of the Second Palomar Sky Survey. This system, called SKICAT, incorporates the latest in machine learning and expert systems software technology, in order to classify the detected objects objectively and uniformly, and facilitate handling of the enormous data sets from digital sky surveys and other sources. The system provides a powerful, integrated environment for the manipulation and scientific investigation of catalogs from virtually any source. It serves three principal functions: image catalog construction, catalog management, and catalog analysis. Through use of the GID3* Decision Tree artificial induction software, SKICAT automates the process of classifying objects within CCD and digitized plate images. To exploit these catalogs, the system also provides tools to merge them into a large, complex database which may be easily queried and modified when new data or better methods of calibrating or classifying become available. The most innovative feature of SKICAT is the facility it provides to experiment with and apply the latest in machine learning technology to the tasks of catalog construction and analysis. SKICAT provides a unique environment for implementing these tools for any number of future scientific purposes. Initial scientific verification and performance tests have been made using galaxy counts and measurements of galaxy clustering from small subsets of the survey data, and a search for very high redshift quasars. All of the tests were successful and produced new and interesting scientific results. Attachments to this report give detailed accounts of the technical aspects of the SKICAT system, and of some of the scientific results achieved to date. We also developed a user-friendly package for multivariate statistical analysis of small and moderate-size data sets, called STATPROG. The package was tested extensively on a number of real scientific applications and has

  19. Solution identification and quantitative analysis of fiber-capacitive drop analyzer based on multivariate statistical methods

    Science.gov (United States)

    Chen, Zhe; Qiu, Zurong; Huo, Xinming; Fan, Yuming; Li, Xinghua

    2017-03-01

    A fiber-capacitive drop analyzer is an instrument which monitors a growing droplet to produce a capacitive opto-tensiotrace (COT). Each COT is an integration of fiber light intensity signals and capacitance signals and can reflect the unique physicochemical property of a liquid. In this study, we propose a solution analytical and concentration quantitative method based on multivariate statistical methods. Eight characteristic values are extracted from each COT. A series of COT characteristic values of training solutions at different concentrations compose a data library of this kind of solution. A two-stage linear discriminant analysis is applied to analyze different solution libraries and establish discriminant functions. Test solutions can be discriminated by these functions. After determining the variety of test solutions, Spearman correlation test and principal components analysis are used to filter and reduce dimensions of eight characteristic values, producing a new representative parameter. A cubic spline interpolation function is built between the parameters and concentrations, based on which we can calculate the concentration of the test solution. Methanol, ethanol, n-propanol, and saline solutions are taken as experimental subjects in this paper. For each solution, nine or ten different concentrations are chosen to be the standard library, and the other two concentrations compose the test group. By using the methods mentioned above, all eight test solutions are correctly identified and the average relative error of quantitative analysis is 1.11%. The method proposed is feasible which enlarges the applicable scope of recognizing liquids based on the COT and improves the concentration quantitative precision, as well.

  20. Risk management and statistical multivariate analysis approach for design and optimization of satranidazole nanoparticles.

    Science.gov (United States)

    Dhat, Shalaka; Pund, Swati; Kokare, Chandrakant; Sharma, Pankaj; Shrivastava, Birendra

    2017-01-01

    Rapidly evolving technical and regulatory landscapes of the pharmaceutical product development necessitates risk management with application of multivariate analysis using Process Analytical Technology (PAT) and Quality by Design (QbD). Poorly soluble, high dose drug, Satranidazole was optimally nanoprecipitated (SAT-NP) employing principles of Formulation by Design (FbD). The potential risk factors influencing the critical quality attributes (CQA) of SAT-NP were identified using Ishikawa diagram. Plackett-Burman screening design was adopted to screen the eight critical formulation and process parameters influencing the mean particle size, zeta potential and dissolution efficiency at 30min in pH7.4 dissolution medium. Pareto charts (individual and cumulative) revealed three most critical factors influencing CQA of SAT-NP viz. aqueous stabilizer (Polyvinyl alcohol), release modifier (Eudragit® S 100) and volume of aqueous phase. The levels of these three critical formulation attributes were optimized by FbD within established design space to minimize mean particle size, poly dispersity index, and maximize encapsulation efficiency of SAT-NP. Lenth's and Bayesian analysis along with mathematical modeling of results allowed identification and quantification of critical formulation attributes significantly active on the selected CQAs. The optimized SAT-NP exhibited mean particle size; 216nm, polydispersity index; 0.250, zeta potential; -3.75mV and encapsulation efficiency; 78.3%. The product was lyophilized using mannitol to form readily redispersible powder. X-ray diffraction analysis confirmed the conversion of crystalline SAT to amorphous form. In vitro release of SAT-NP in gradually pH changing media showed 95%) in pH7.4 in next 3h, indicative of burst release after a lag time. This investigation demonstrated effective application of risk management and QbD tools in developing site-specific release SAT-NP by nanoprecipitation.

  1. Environmental controls on microbial abundance and activity on the greenland ice sheet: a multivariate analysis approach.

    Science.gov (United States)

    Stibal, Marek; Telling, Jon; Cook, Joe; Mak, Ka Man; Hodson, Andy; Anesio, Alexandre M

    2012-01-01

    Microbes in supraglacial ecosystems have been proposed to be significant contributors to regional and possibly global carbon cycling, and quantifying the biogeochemical cycling of carbon in glacial ecosystems is of great significance for global carbon flow estimations. Here we present data on microbial abundance and productivity, collected along a transect across the ablation zone of the Greenland ice sheet (GrIS) in summer 2010. We analyse the relationships between the physical, chemical and biological variables using multivariate statistical analysis. Concentrations of debris-bound nutrients increased with distance from the ice sheet margin, as did both cell numbers and activity rates before reaching a peak (photosynthesis) or a plateau (respiration, abundance) between 10 and 20 km from the margin. The results of productivity measurements suggest an overall net autotrophy on the GrIS and support the proposed role of ice sheet ecosystems in carbon cycling as regional sinks of CO(2) and places of production of organic matter that can be a potential source of nutrients for downstream ecosystems. Principal component analysis based on chemical and biological data revealed three clusters of sites, corresponding to three 'glacier ecological zones', confirmed by a redundancy analysis (RDA) using physical data as predictors. RDA using data from the largest 'bare ice zone' showed that glacier surface slope, a proxy for melt water flow, accounted for most of the variation in the data. Variation in the chemical data was fully explainable by the determined physical variables. Abundance of phototrophic microbes and their proportion in the community were identified as significant controls of the carbon cycling-related microbial processes.

  2. Multivariate analysis of behavioural response experiments in humpback whales (Megaptera novaeangliae).

    Science.gov (United States)

    Dunlop, Rebecca A; Noad, Michael J; Cato, Douglas H; Kniest, Eric; Miller, Patrick J O; Smith, Joshua N; Stokes, M Dale

    2013-03-01

    The behavioural response study (BRS) is an experimental design used by field biologists to determine the function and/or behavioural effects of conspecific, heterospecific or anthropogenic stimuli. When carrying out these studies in marine mammals it is difficult to make basic observations and achieve sufficient samples sizes because of the high cost and logistical difficulties. Rarely are other factors such as social context or the physical environment considered in the analysis because of these difficulties. This paper presents results of a BRS carried out in humpback whales to test the response of groups to one recording of conspecific social sounds and an artificially generated tone stimulus. Experiments were carried out in September/October 2004 and 2008 during the humpback whale southward migration along the east coast of Australia. In total, 13 'tone' experiments, 15 'social sound' experiments (using one recording of social sounds) and three silent controls were carried out over two field seasons. The results (using a mixed model statistical analysis) suggested that humpback whales responded differently to the two stimuli, measured by changes in course travelled and dive behaviour. Although the response to 'tones' was consistent, in that groups moved offshore and surfaced more often (suggesting an aversion to the stimulus), the response to 'social sounds' was highly variable and dependent upon the composition of the social group. The change in course and dive behaviour in response to 'tones' was found to be related to proximity to the source, the received signal level and signal-to-noise ratio (SNR). This study demonstrates that the behavioural responses of marine mammals to acoustic stimuli are complex. In order to tease out such multifaceted interactions, the number of replicates and factors measured must be sufficient for multivariate analysis.

  3. Multivariate statistical analysis software technologies for astrophysical research involving large data bases

    Science.gov (United States)

    Djorgovski, S. George

    1994-01-01

    We developed a package to process and analyze the data from the digital version of the Second Palomar Sky Survey. This system, called SKICAT, incorporates the latest in machine learning and expert systems software technology, in order to classify the detected objects objectively and uniformly, and facilitate handling of the enormous data sets from digital sky surveys and other sources. The system provides a powerful, integrated environment for the manipulation and scientific investigation of catalogs from virtually any source. It serves three principal functions: image catalog construction, catalog management, and catalog analysis. Through use of the GID3* Decision Tree artificial induction software, SKICAT automates the process of classifying objects within CCD and digitized plate images. To exploit these catalogs, the system also provides tools to merge them into a large, complete database which may be easily queried and modified when new data or better methods of calibrating or classifying become available. The most innovative feature of SKICAT is the facility it provides to experiment with and apply the latest in machine learning technology to the tasks of catalog construction and analysis. SKICAT provides a unique environment for implementing these tools for any number of future scientific purposes. Initial scientific verification and performance tests have been made using galaxy counts and measurements of galaxy clustering from small subsets of the survey data, and a search for very high redshift quasars. All of the tests were successful, and produced new and interesting scientific results. Attachments to this report give detailed accounts of the technical aspects for multivariate statistical analysis of small and moderate-size data sets, called STATPROG. The package was tested extensively on a number of real scientific applications, and has produced real, published results.

  4. Multivariate analysis on unilateral cleft lip and palate treatment outcome by EUROCRAN index: A retrospective study.

    Science.gov (United States)

    Yew, Ching Ching; Alam, Mohammad Khursheed; Rahman, Shaifulizan Abdul

    2016-10-01

    This study is to evaluate the dental arch relationship and palatal morphology of unilateral cleft lip and palate patients by using EUROCRAN index, and to assess the factors that affect them using multivariate statistical analysis. A total of one hundred and seven patients from age five to twelve years old with non-syndromic unilateral cleft lip and palate were included in the study. These patients have received cheiloplasty and one stage palatoplasty surgery but yet to receive alveolar bone grafting procedure. Five assessors trained in the use of the EUROCRAN index underwent calibration exercise and ranked the dental arch relationships and palatal morphology of the patients' study models. For intra-rater agreement, the examiners scored the models twice, with two weeks interval in between sessions. Variable factors of the patients were collected and they included gender, site, type and, family history of unilateral cleft lip and palate; absence of lateral incisor on cleft side, cheiloplasty and palatoplasty technique used. Associations between various factors and dental arch relationships were assessed using logistic regression analysis. Dental arch relationship among unilateral cleft lip and palate in local population had relatively worse scoring than other parts of the world. Crude logistics regression analysis did not demonstrate any significant associations among the various socio-demographic factors, cheiloplasty and palatoplasty techniques used with the dental arch relationship outcome. This study has limitations that might have affected the results, example: having multiple operators performing the surgeries and the inability to access the influence of underlying genetic predisposed cranio-facial variability. These may have substantial influence on the treatment outcome. The factors that can affect unilateral cleft lip and palate treatment outcome is multifactorial in nature and remained controversial in general. Copyright © 2016 Elsevier Ireland Ltd. All

  5. Integrated GIS and multivariate statistical analysis for regional scale assessment of heavy metal soil contamination: A critical review.

    Science.gov (United States)

    Hou, Deyi; O'Connor, David; Nathanail, Paul; Tian, Li; Ma, Yan

    2017-09-19

    Heavy metal soil contamination is associated with potential toxicity to humans or ecotoxicity. Scholars have increasingly used a combination of geographical information science (GIS) with geostatistical and multivariate statistical analysis techniques to examine the spatial distribution of heavy metals in soils at a regional scale. A review of such studies showed that most soil sampling programs were based on grid patterns and composite sampling methodologies. Many programs intended to characterize various soil types and land use types. The most often used sampling depth intervals were 0-0.10 m, or 0-0.20 m, below surface; and the sampling densities used ranged from 0.0004 to 6.1 samples per km(2), with a median of 0.4 samples per km(2). The most widely used spatial interpolators were inverse distance weighted interpolation and ordinary kriging; and the most often used multivariate statistical analysis techniques were principal component analysis and cluster analysis. The review also identified several determining and correlating factors in heavy metal distribution in soils, including soil type, soil pH, soil organic matter, land use type, Fe, Al, and heavy metal concentrations. The major natural and anthropogenic sources of heavy metals were found to derive from lithogenic origin, roadway and transportation, atmospheric deposition, wastewater and runoff from industrial and mining facilities, fertilizer application, livestock manure, and sewage sludge. This review argues that the full potential of integrated GIS and multivariate statistical analysis for assessing heavy metal distribution in soils on a regional scale has not yet been fully realized. It is proposed that future research be conducted to map multivariate results in GIS to pinpoint specific anthropogenic sources, to analyze temporal trends in addition to spatial patterns, to optimize modeling parameters, and to expand the use of different multivariate analysis tools beyond principal component analysis

  6. The Multi-Isotope Process Monitor: Multivariate Analysis of Gamma Spectra

    Energy Technology Data Exchange (ETDEWEB)

    Orton, Christopher R.; Rutherford, Crystal E.; Fraga, Carlos G.; Schwantes, Jon M.

    2011-10-30

    The International Atomic Energy Agency (IAEA) has established international safeguards standards for fissionable material at spent fuel reprocessing plants to ensure that significant quantities of nuclear material are not diverted from these facilities. Currently, methods to verify material control and accountancy (MC&A) at these facilities require time-consuming and resource-intensive destructive assay (DA). The time delay between sampling and subsequent DA provides a potential opportunity to divert the material out of the appropriate chemical stream. Leveraging new on-line nondestructive assay (NDA) techniques in conjunction with the traditional and highly precise DA methods may provide a more timely, cost-effective and resource efficient means for MC&A verification at such facilities. Pacific Northwest National Laboratory (PNNL) is developing on-line NDA process monitoring technologies, including the Multi-Isotope Process (MIP) Monitor. The MIP Monitor uses gamma spectroscopy and pattern recognition software to identify off-normal conditions in process streams. Recent efforts have been made to explore the basic limits of using multivariate analysis techniques on gamma-ray spectra. This paper will provide an overview of the methods and report our on-going efforts to develop and demonstrate the technology.

  7. A multivariate analysis of biophysical parameters of tallgrass prairie among land management practices and years

    Science.gov (United States)

    Griffith, J.A.; Price, K.P.; Martinko, E.A.

    2001-01-01

    Six treatments of eastern Kansas tallgrass prairie - native prairie, hayed, mowed, grazed, burned and untreated - were studied to examine the biophysical effects of land management practices on grasslands. On each treatment, measurements of plant biomass, leaf area index, plant cover, leaf moisture and soil moisture were collected. In addition, measurements were taken of the Normalized Difference Vegetation Index (NDVI), which is derived from spectral reflectance measurements. Measurements were taken in mid-June, mid-July and late summer of 1990 and 1991. Multivariate analysis of variance was used to determine whether there were differences in the set of variables among treatments and years. Follow-up tests included univariate t-tests to determine which variables were contributing to any significant difference. Results showed a significant difference (p tests showed, however, that only some variables, primarily soil moisture, were contributing to this difference. We conclude that biomass and % plant cover show the best potential to serve as long-term indicators of grassland condition as they generally were sensitive to effects of different land management practices but not to yearly change in weather conditions. NDVI was insensitive to precipitation differences between years in July for most treatments, but was not in the native prairie. Choice of sampling time is important for these parameters to serve effectively as indicators.

  8. Supervised multivariate analysis of sequence groups to identify specificity determining residues

    Directory of Open Access Journals (Sweden)

    Higgins Desmond G

    2007-04-01

    Full Text Available Abstract Background Proteins that evolve from a common ancestor can change functionality over time, and it is important to be able identify residues that cause this change. In this paper we show how a supervised multivariate statistical method, Between Group Analysis (BGA, can be used to identify these residues from families of proteins with different substrate specifities using multiple sequence alignments. Results We demonstrate the usefulness of this method on three different test cases. Two of these test cases, the Lactate/Malate dehydrogenase family and Nucleotidyl Cyclases, consist of two functional groups. The other family, Serine Proteases consists of three groups. BGA was used to analyse and visualise these three families using two different encoding schemes for the amino acids. Conclusion This overall combination of methods in this paper is powerful and flexible while being computationally very fast and simple. BGA is especially useful because it can be used to analyse any number of functional classes. In the examples we used in this paper, we have only used 2 or 3 classes for demonstration purposes but any number can be used and visualised.

  9. Multivariate analysis and optimization of linear periodically time-variable circuits at the enviroment of MAOPCs

    Directory of Open Access Journals (Sweden)

    Yu. I. Shapovalov

    2015-03-01

    Full Text Available Introduction. The architecture of MAOPCs functions system and examples of its application for solving the tasks of multivariate analysis of linear periodically time-variable (LPTV circuits based on the frequency symbolic method are considered in this paper. The method is based on approximation of transfer functions of LPTV circuits in the form of trigonometric polynomials of the Fourier. The MAOPCs functions system is implemented in the environment of MATLAB. Architecture and functions of the system MAOPCs. The system consists of 17 functions that are implemented in the environment of MATLAB. Each function has arguments and global variables and carries out over them identified transformation. Functions and global variables form the input data program for research LPTV circuit and should be defined (set at the time of calling the function. Conclusions. The MAOPCs functions system enables to investigate LPTV circuits, setting in program input data the algorithms for their research and to use a strong symbolic apparatus and other standard functions of the package MATLAB in full, without understanding the deep of mathematical apparatus of implemented methods.

  10. Forecasting daily source air quality using multivariate statistical analysis and radial basis function networks.

    Science.gov (United States)

    Sun, Gang; Hoff, Steven J; Zelle, Brian C; Nelson, Minda A

    2008-12-01

    It is vital to forecast gas and particle matter concentrations and emission rates (GPCER) from livestock production facilities to assess the impact of airborne pollutants on human health, ecological environment, and global warming. Modeling source air quality is a complex process because of abundant nonlinear interactions between GPCER and other factors. The objective of this study was to introduce statistical methods and radial basis function (RBF) neural network to predict daily source air quality in Iowa swine deep-pit finishing buildings. The results show that four variables (outdoor and indoor temperature, animal units, and ventilation rates) were identified as relative important model inputs using statistical methods. It can be further demonstrated that only two factors, the environment factor and the animal factor, were capable of explaining more than 94% of the total variability after performing principal component analysis. The introduction of fewer uncorrelated variables to the neural network would result in the reduction of the model structure complexity, minimize computation cost, and eliminate model overfitting problems. The obtained results of RBF network prediction were in good agreement with the actual measurements, with values of the correlation coefficient between 0.741 and 0.995 and very low values of systemic performance indexes for all the models. The good results indicated the RBF network could be trained to model these highly nonlinear relationships. Thus, the RBF neural network technology combined with multivariate statistical methods is a promising tool for air pollutant emissions modeling.

  11. Investigation of acupoint specificity by multivariate granger causality analysis from functional MRI data.

    Science.gov (United States)

    Feng, Yuanyuan; Bai, Lijun; Zhang, Wensheng; Xue, Ting; Ren, Yanshuang; Zhong, Chongguang; Wang, Hu; You, Youbo; Liu, Zhenyu; Dai, Jianping; Liu, Yijun; Tian, Jie

    2011-07-01

    To investigate the acupoint specificity by exploring the effective connectivity patterns of the poststimulus resting brain networks modulated by acupuncture at the PC6, with the same meridian acupoint PC7 and different meridian acupoint GB37. The functional MRI (fMRI) study was performed in 36 healthy right-handed subjects receiving acupuncture at three acupoints, respectively. Due to the sustained effects of acupuncture, a novel experimental paradigm using the nonrepeated event-related (NRER) design was adopted. Psychophysical responses (deqi sensations) were also assessed. Finally, a newly multivariate Granger causality analysis (mGCA) was used to analyze effective connectivity patterns of the resting fMRI data taken following acupuncture at three acupoints. Following acupuncture at PC6, the red nucleus and substantia nigra emerged as central hubs, in comparison with the fusiform gyrus following acupuncture at GB37. Red nucleus was also a target following acupuncture at PC7, but with fewer inputs than those of PC6. In addition, the most important target following acupuncture at PC7 was located at the parahippocampus. Our findings demonstrated that acupuncture at different acupoints may exert heterogeneous modulatory effects on the causal interactions of brain areas during the poststimulus resting state. These preliminary findings provided a clue to elucidate the relatively function-oriented specificity of acupuncture effects. Copyright © 2011 Wiley-Liss, Inc.

  12. Modeling Multi-Variate Gaussian Distributions and Analysis of Higgs Boson Couplings with the ATLAS Detector

    Science.gov (United States)

    Krohn, Olivia; Armbruster, Aaron; Gao, Yongsheng; Atlas Collaboration

    2017-01-01

    Software tools developed for the purpose of modeling CERN LHC pp collision data to aid in its interpretation are presented. Some measurements are not adequately described by a Gaussian distribution; thus an interpretation assuming Gaussian uncertainties will inevitably introduce bias, necessitating analytical tools to recreate and evaluate non-Gaussian features. One example is the measurements of Higgs boson production rates in different decay channels, and the interpretation of these measurements. The ratios of data to Standard Model expectations (μ) for five arbitrary signals were modeled by building five Poisson distributions with mixed signal contributions such that the measured values of μ are correlated. Algorithms were designed to recreate probability distribution functions of μ as multi-variate Gaussians, where the standard deviation (σ) and correlation coefficients (ρ) are parametrized. There was good success with modeling 1-D likelihood contours of μ, and the multi-dimensional distributions were well modeled within 1- σ but the model began to diverge after 2- σ due to unmerited assumptions in developing ρ. Future plans to improve the algorithms and develop a user-friendly analysis package will also be discussed. NSF International Research Experiences for Students

  13. Breast tissue classification using x-ray scattering measurements and multivariate data analysis

    Energy Technology Data Exchange (ETDEWEB)

    Ryan, Elaine A; Farquharson, Michael J [School of Allied Health Sciences, City University, Charterhouse Square, London EC1M 6PA (United Kingdom)

    2007-11-21

    This study utilized two radiation scatter interactions in order to differentiate malignant from non-malignant breast tissue. These two interactions were Compton scatter, used to measure the electron density of the tissues, and coherent scatter to obtain a measure of structure. Measurements of these parameters were made using a laboratory experimental set-up comprising an x-ray tube and HPGe detector. The breast tissue samples investigated comprise five different tissue classifications: adipose, malignancy, fibroadenoma, normal fibrous tissue and tissue that had undergone fibrocystic change. The coherent scatter spectra were analysed using a peak fitting routine, and a technique involving multivariate analysis was used to combine the peak fitted scatter profile spectra and the electron density values into a tissue classification model. The number of variables used in the model was refined by finding the sensitivity and specificity of each model and concentrating on differentiating between two tissues at a time. The best model that was formulated had a sensitivity of 54% and a specificity of 100%.

  14. Breast tissue classification using x-ray scattering measurements and multivariate data analysis

    Science.gov (United States)

    Ryan, Elaine A.; Farquharson, Michael J.

    2007-11-01

    This study utilized two radiation scatter interactions in order to differentiate malignant from non-malignant breast tissue. These two interactions were Compton scatter, used to measure the electron density of the tissues, and coherent scatter to obtain a measure of structure. Measurements of these parameters were made using a laboratory experimental set-up comprising an x-ray tube and HPGe detector. The breast tissue samples investigated comprise five different tissue classifications: adipose, malignancy, fibroadenoma, normal fibrous tissue and tissue that had undergone fibrocystic change. The coherent scatter spectra were analysed using a peak fitting routine, and a technique involving multivariate analysis was used to combine the peak fitted scatter profile spectra and the electron density values into a tissue classification model. The number of variables used in the model was refined by finding the sensitivity and specificity of each model and concentrating on differentiating between two tissues at a time. The best model that was formulated had a sensitivity of 54% and a specificity of 100%.

  15. Quality assessment of pharmaceutical tablet samples using Fourier transform near infrared spectroscopy and multivariate analysis

    Science.gov (United States)

    Kandpal, Lalit Mohan; Tewari, Jagdish; Gopinathan, Nishanth; Stolee, Jessica; Strong, Rick; Boulas, Pierre; Cho, Byoung-Kwan

    2017-09-01

    Determination of the content uniformity, assessed by the amount of an active pharmaceutical ingredient (API), and hardness of pharmaceutical materials is important for achieving a high-quality formulation and to ensure the intended therapeutic effects of the end-product. In this work, Fourier transform near infrared (FT-NIR) spectroscopy was used to determine the content uniformity and hardness of a pharmaceutical mini-tablet and standard tablet samples. Tablet samples were scanned using an FT-NIR instrument and tablet spectra were collected at wavelengths of 1000-2500 nm. Furthermore, multivariate analysis was applied to extract the relationship between the FT-NIR spectra and the measured parameters. The results of FT-NIR spectroscopy for API and hardness prediction were as precise as the reference high-performance liquid chromatography and mechanical hardness tests. For the prediction of mini-tablet API content, the highest coefficient of determination for the prediction (R2p) was found to be 0.99 with a standard error of prediction (SEP) of 0.72 mg. Moreover, the standard tablet hardness measurement had a R2p value of 0.91 with an SEP of 0.25 kg. These results suggest that FT-NIR spectroscopy is an alternative and accurate nondestructive measurement tool for the detection of the chemical and physical properties of pharmaceutical samples.

  16. Multivariate analysis of adaptive capacity for upper thermal limits in Drosophila simulans.

    Science.gov (United States)

    van Heerwaarden, B; Sgrò, C M

    2013-04-01

    Thermal tolerance is an important factor influencing the distribution of ectotherms, but our understanding of the ability of species to evolve different thermal limits is limited. Based on univariate measures of adaptive capacity, it has recently been suggested that species may have limited evolutionary potential to extend their upper thermal limits under ramping temperature conditions that better reflect heat stress in nature. To test these findings more broadly, we used a paternal half-sibling breeding design to estimate the multivariate evolutionary potential for upper thermal limits in Drosophila simulans. We assessed heat tolerance using static (basal and hardened) and ramping assays. Our analyses revealed significant evolutionary potential for all three measures of heat tolerance. Additive genetic variances were significantly different from zero for all three traits. Our G matrix analysis revealed that all three traits would contribute to a response to selection for increased heat tolerance. Significant additive genetic covariances and additive genetic correlations between static basal and hardened heat-knockdown time, marginally nonsignificant between static basal and ramping heat-knockdown time, indicate that direct and correlated responses to selection for increased upper thermal limits are possible. Thus, combinations of all three traits will contribute to the evolution of upper thermal limits in response to selection imposed by a warming climate. Reliance on univariate estimates of evolutionary potential may not provide accurate insight into the ability of organisms to evolve upper thermal limits in nature.

  17. Multivariate Meta-Analysis of Brain-Mass Correlations in Eutherian Mammals

    Directory of Open Access Journals (Sweden)

    Charlene Steinhausen

    2016-09-01

    Full Text Available The general assumption that brain size differences are an adequate proxy for subtler differences in brain organization turned neurobiologists towards the question why some groups of mammals such as primates, elephants, and whales have such remarkably large brains. In this meta-analysis, an extensive sample of eutherian mammals (115 species distributed in 14 orders provided data about several different biological traits and measures of brain size such as absolute brain mass (AB, relative brain mass (RB; quotient from AB and body mass, and encephalization quotient (EQ. These data were analyzed by established multivariate statistics without taking specific phylogenetic information into account. Species with high AB tend to (1 feed on protein-rich nutrition, (2 have a long lifespan, (3 delay sexual maturity, and (4 have long and rare pregnancies with small litter sizes. Animals with high RB usually have (1 a short life span, (2 reach sexual maturity early, and (3 have short and frequent gestations. Moreover males of species with high RB also have few potential sexual partners. In contrast, animals with high EQs have (1 a high number of potential sexual partners, (2 delayed sexual maturity, and (3 rare gestations with small litter sizes. Based on these correlations, we conclude that Eutheria with either high AB or high EQ occupy high positions in the network of food chains (high trophic levels. Eutheria of low trophic levels can develop a high RB only if they have small body masses.

  18. Near and mid infrared spectroscopy and multivariate data analysis in studies of oxidation of edible oils.

    Science.gov (United States)

    Wójcicki, Krzysztof; Khmelinskii, Igor; Sikorski, Marek; Sikorska, Ewa

    2015-11-15

    Infrared spectroscopic techniques and chemometric methods were used to study oxidation of olive, sunflower and rapeseed oils. Accelerated oxidative degradation of oils at 60°C was monitored using peroxide values and FT-MIR ATR and FT-NIR transmittance spectroscopy. Principal component analysis (PCA) facilitated visualization and interpretation of spectral changes occurring during oxidation. Multivariate curve resolution (MCR) method found three spectral components in the NIR and MIR spectral matrix, corresponding to the oxidation products, and saturated and unsaturated structures. Good quantitative relation was found between peroxide value and contribution of oxidation products evaluated using MCR--based on NIR (R(2) = 0.890), MIR (R(2) = 0.707) and combined NIR and MIR (R(2) = 0.747) data. Calibration models for prediction peroxide value established using partial least squares (PLS) regression were characterized for MIR (R(2) = 0.701, RPD = 1.7), NIR (R(2) = 0.970, RPD = 5.3), and combined NIR and MIR data (R(2) = 0.954, RPD = 3.1). Copyright © 2015 Elsevier Ltd. All rights reserved.

  19. Multivariate analysis of the risk for pulmonary complication after gastrointestinal surgery

    Institute of Scientific and Technical Information of China (English)

    Shan-Ping Jiang; Zhi-Ying Li; Li-Wen Huang; Wei Zhang; Zhi-Qiang Lu; Zhi-Yong Zheng

    2005-01-01

    AIM: To identify the risk factors for postoperative pulmonary complications (PPC) after gastrointestinal surgery.METHODS: A total of 1 002 patients undergoing gastrointestinal surgery in the Second Affiliated Hospital, Sun Yat-Sen University, during December 1999 and December 2003, were retrospectively studied.RESULTS: The overall incidence of PPC was 22.8% (228/ 1 002). Multivariate logistic analysis identified nine risk factors associated with PPC, including age odds ratio (OR = 1.040) history of respiratory diseases (OR = 2.976),serum albumin (OR = 0.954), chemotherapy 2 wk before operation (OR = 3.214), volume of preoperative erythrocyte transfusion (OR = 1.002), length of preoperative antibiotic therapy (OR = 1.072), intraoperative intratracheal intubation (OR = 1.002), nasogastric intubation (OR = 1.050) and postoperative mechanical ventilation (OR = 1.878). Logistic regression equation for predicting the risk of PPC was P(1) = 1/[1+e-(-3.488+ 0.039× Y+1.090×Rd+0.001×Rbc-0.047×Alb+0.002×Lii+0.049×Lni+0.630×Lmv+0.070×Dat+ 1.168×Ct)].CONCLUSION: Old patients are easier to develop PPC.

  20. Revealing the metabonomic variation of rosemary extracts using 1H NMR spectroscopy and multivariate data analysis.

    Science.gov (United States)

    Xiao, Chaoni; Dai, Hui; Liu, Hongbing; Wang, Yulan; Tang, Huiru

    2008-11-12

    The molecular compositions of rosemary ( Rosmarinus officinalis L.) extracts and their dependence on extraction solvents, seasons, and drying processes were systematically characterized using NMR spectroscopy and multivariate data analysis. The results showed that the rosemary metabonome was dominated by 33 metabolites including sugars, amino acids, organic acids, polyphenolic acids, and diterpenes, among which quinate, cis-4-glucosyloxycinnamic acid, and 3,4,5-trimethoxyphenylmethanol were found in rosemary for the first time. Compared with water extracts, the 50% aqueous methanol extracts contained higher levels of sucrose, succinate, fumarate, malonate, shikimate, and phenolic acids, but lower levels of fructose, glucose, citrate, and quinate. Chloroform/methanol was an excellent solvent for selective extraction of diterpenes. From February to August, the levels of rosmarinate and quinate increased, whereas the sucrose level decreased. The sun-dried samples contained higher concentrations of rosmarinate, sucrose, and some amino acids but lower concentrations of glucose, fructose, malate, succinate, lactate, and quinate than freeze-dried ones. These findings will fill the gap in the understanding of rosemary composition and its variations.